Systems and methods for processing data flows

ABSTRACT

A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps. Exposing threats and intrusions within packet payload at or near real-time rates enhances network security from both external and internal sources while ensuring security policy is rigorously applied to data and system resources. Intrusion Detection and Protection (IDP) is provided by a flow processing facility that processes a data flow to address patterns relevant to a variety of types of network and data integrity threats that relate to computer systems, including computer networks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplications, each of which is hereby incorporated by reference in itsentirety: U.S. App. No. 60/749,915 filed on Dec. 13, 2005 and entitled“HIGH SPEED PATTERN MATCHING”; U.S. App. No. 60/750,664 filed on Dec.14, 2005 and entitled “USING NEURAL NETWORKS TO DETECT ANOMALOUSCOMMUNICATIONS FLOWS”; U.S. App. No. 60/795,886 filed on Apr. 27, 2006and entitled “SYSTEM AND METHODS OF FLOW PROCESSING FOR UNIFIED THREATMANAGEMENT”; U.S. App. No. 60/795,885 filed on Apr. 27, 2006 andentitled “SYSTEM AND METHODS OF FLOW PROCESSING FOR VIRUS PROTECTION”;U.S. App. No. 60/795,708 filed on Apr. 27, 2006 and entitled “SYSTEMSAND METHODS FOR FLOW PROCESSING”; U.S. App. No. 60/795,712 filed on Apr.27, 2006 and entitled “SYSTEM AND METHODS OF FLOW PROCESSING WITHMACHINE LEARNING”; and U.S. App. No. 60/795,707 filed Apr. 27, 2006 andentitled “SYSTEMS AND METHODS OF FLOW PROCESSING FOR NETWORK FIREWALLS”.

This application is a continuation-in-part of the following U.S. patentapplications, each of which is incorporated by reference in itsentirety: U.S. application Ser. No. 11/174,181 filed on Apr. 24, 2001and entitled “FLOW SCHEDULING FOR NETWORK APPLICATION APPARATUS,” andU.S. application Ser. No. 11/173,923 filed on Apr. 24, 2001 and entitled“NETWORK APPLICATION APPARATUS.”

BACKGROUND

1. Field

This invention is in the field of computer security and protection.Specifically, it is in the field of protecting computer systems fromviruses, attacks from hackers and other unauthorized intrusions,spyware, spam, phishing and other scams, malicious activities and code.

2. Description of the Related Art

Methods providing security for computer systems have been developed,which address disparate threats to the systems, such threats includingcomputer viruses, attacks by hackers, spyware, phishing, spam, intrusiononto a computer network by unauthorized users, and others. Products havebeen developed that separately address each of the most prevalent typeof threats, and, more recently, those products have been joined togetherin suites of applications, where each application addresses a differentkind of threat. The latter approach, known as unified threat management,offers more comprehensive protection against threats; however, theprotection comes at the expense of processing resources, as eachapplication in a unified threat management suite must use suchresources.

One type of standalone products, known as firewalls, addresses andprotects against these kinds of threats; however, this protection comeseither at the expense of processing resources (in cases where a softwarefirewall product must be installed on a server) or at the expense ofoperational complexity (in cases where the firewall product is embodiedin a dedicated network device). A need exists for more convenient andeffective firewall techniques.

Methods providing network switching and security services for computersystems have been developed, which address many aspects of networking,internetworking, access control, security, and other such services.Products have been developed that separately provide each of the mostneeded services. More recently, some of these products have been joinedtogether in suites of applications or monolithic networking hardware,where each application provides a different service or where thehardware is more or less hardwired to provide a set of services. A needexists for improved ways of providing switching and security services.

Network security is also being threatened from ever increasinglysophisticated threats that attack any and all vulnerabilities of networkcommunication systems. Packet switched network communication systemsremain vulnerable to security threats in part due to their layeredprotocol schemes. Detecting and preventing threats and intrusions byinspecting only a packet header does not detect threats that attackapplication level information transported in and across packets.Therefore, needs exist for improved ways of providing switching andsecurity services for networked environments.

Another need is for better intrusion detection and prevention.Companies' computing systems are more interconnected than ever, with thepromise that network expansion will only continue. Companies depend uponthe Internet for additional business-critical activities like supplychain integration, long-distance communications, and remote siteconnectivity. While this helps boost productivity, each Internet-basedendeavor potentially opens another door to outside hackers and maliciouscode attacks. Companies are also faced with legal and ethicalresponsibility of their information and network security. Regulatorystatues such as HIPAA (Health Insurance Portability and Accountability)further require comprehensive network security. As a result, companiesmust grapple with how to keep their network safe, without sacrificinggrowth or productivity.

Systems that provide only intrusion detection may have substantialdrawbacks in this environment including false alarms, low manageability,high maintenance, and no prevention of attacks. False alarms maymanifest as large quantities of records that require manual filtering, acostly and error prone process. An intrusion detection system thatrequires substantial time and effort to maintain detection sensors,security policies, and intrusion lists may contribute to poor intrusiondetection.

A need exists for more effective unified threat management techniques,including techniques that address critical types of threats. Criticalthreats include, for example, viruses, network security holes, networkcommunications, content inspection, intrusions, and other attacks thatcan be blocked by firewalls.

SUMMARY

Provided herein are methods and systems for unified threat management,including unified threat management using a flow processing facilitythat processes a data flow to address patterns relevant to a variety oftypes of threats that relate to computer systems, including computernetworks. The flow processing facility may use a set of artificialneurons for pattern recognition, such as a self-organizing map.

This disclosure describes unified threat management methods and systemsin which disparate threat management methods are implemented in a singleflow processing architecture. In embodiments, the flow processingarchitecture may use a set of artificial neurons, such as aself-organizing map (SOM) or neural net, to process data flows, whereinthe set of artificial neurons enables recognition of patterns that arerelevant to identifying threats of disparate types, including threatsrelevant to intrusion detection, intrusion protection, anti-virusprotection, anti-spyware protection, and anti-spam protection, as wellas other types of threats, such as related to phishing or unauthorizeduse of computer network resources.

The methods and systems disclosed herein for securing a computerresource include methods systems for providing a flow processingfacility for processing a data flow, and configuring the flow processingfacility to recognize patterns in the data flow, wherein the patternsare relevant to recognition of the presence of at least two of a virus,a spam communication, a hacker's attack, spyware, and intrusion on acomputer network and wherein the flow processing facility recognizespatterns using a set of artificial neurons. In embodiments, the patternsare relevant to recognition of a virus and a spam communication. Inembodiments, the patterns are relevant to recognition of a virus and ahacker's attack. In embodiments, the patterns are relevant torecognition of a virus and spyware. In embodiments, the patterns arerelevant to recognition of a virus and intrusion on a computer network.In embodiments, the patterns are relevant to recognition of a spamcommunication and a hacker's attack. In embodiments, the patterns arerelevant to recognition of a spam communication and spyware. Inembodiments, the patterns are relevant to recognition of a spamcommunication and intrusion on a computer network. In embodiments, thepatterns are relevant to recognition of a hacker's attack and spyware.In embodiments, the patterns are relevant to recognition of a hacker'sattack and intrusion on a computer network. In embodiments, the patternsare relevant to recognition of spyware and intrusion on a computernetwork. In embodiments, the set of artificial neurons is aself-organizing map or a neural network.

Provided are systems and methods relating to an architecture of a flowprocessing facility, including hardware configurations, process flowsand data flows. The flow processing facility may include amachine-learning algorithm for characterizing the data flows. Themachine-learning algorithm may include a set of artificial neurons, suchas and without limitation a SOM. The architecture may be composed ofmodules, such as a control processor, a network processor, anapplication processor, a chassis, and so forth. The flow processingfacility may provide switching, security, and other networkapplications.

The flow processing facility may provide a network service by processinga data flow, recognizing patterns in the data flow, receiving the dataflow from a network interface, characterizing the data flow within adata flow engine, and routing the data flow. Characterizing the dataflow may be achieved with the aid of a set of artificial neurons.Routing the data flow may be associated with a result of characterizingthe data flow. The network interface may be a computer network, whichmay consist of an internetwork, an intranet, a VPN, a personal computer,a computer resource, and so forth. The network interface may be awireless network or a telecommunications network. The data flow enginemay be associated with an application processor module, which mayinclude an application. The data flow engine may include a data flowprocessor, which may include a machine learning logic facility, amachine learning acceleration hardware, a content search logic, and soforth. The data flow engine may include a cell generator, a cell router,and so forth. The cell router may be associated with an applicationprocessor module, which itself may include an application.

External web access to information on a network is critical to theefficient and effective workings of enterprises. Employees, partners,customers, and remote users need timely access using a wide variety ofcommunication methods and devices from all locations. Additionally, theconfidentially and integrity of network resources such as intellectualproperty, competitively advantaged data, regulated or personal data mustbe maintained in this open environment. However, threats of attack,intrusion, and espionage may come in a wide variety of forms such asspyware, keystroke loggers, and Trojans, while malware such as worms andviruses must also be detected and prevented.

Network security management involves balancing a complex array ofnetwork participant needs. Internal and external users have preferencesand needs for effective productivity, while the corporation has needsfor data integrity and expandability. There are regulatory needs forconfidential and financial data protection that must be balanced againstclient (customer) needs for timely access to information about productsand services (including financial transactions). These needs are also tobe balanced against protecting network integrity and reliability fromthreats from external (internet) and internal users. Providing a networksecurity solution that effectively delivers all of one participant'saccess needs may impose constraints on one or many other participants'needs such as making critical aspects of the network vulnerable tointrusions.

Since all, or nearly all of the data accessed and used by internalusers, external users, clients, servers, vendors, and the like passesthrough an organization's network, segmenting the network to address thevarious needs of the network participants can be costly because of thesubstantial expense associated with hardware security facilities. Also,segmenting may not relieve the constraints sufficiently to justify thisexpense. In addition, management of a myriad of segmented, networkmanagement devices increases complexity which may create newopportunities for segments being vulnerable to intrusion.

While physically separating network participants is neither practicalnor in most cases possible while still delivering effective businesssolutions through the network, separation of aspects of a networksecurity management system may be beneficial. An approach to allowmanaged separation of aspects of a network security system based onparticipant criteria may include virtualization of the network. Networkvirtualization may allow one or more participants (or participant types)to be logically connected to the network through a virtual networkconnection within a network security system such as the flow processingfacility.

Network security may address both external threats and internal threats.Attacks from internal resources that may be properly authenticated toconnect to a network may include laptops, smart mobile devices, PDS, andother devices that may reconnect to the network throughout the work day.Any threat that propagates from one networked client to another may beintroduced from an infected client within a network.

This application describes a flow processing facility used in computersecurity with particular embodiments relating to content inspection.Referring generally to the present invention, in a networked computerenvironment using packet switching communication, network securitypolicies may be enforced by inspecting a packet and, as necessary,responding to a result of the packet inspection. The packet inspectionmay be directed at a header of the packet and/or a payload of thepacket. Such packet inspection may be performed at any and all layers ofa network communication protocol stack (such as and without limitationthe Internet Protocol stack). Inspecting the payload of the packet maybe referred to as “deep packet inspection” or “payload inspection.” Inany case, any and all packet inspection may be directed at theinspection of data that encompasses a packet or flow of packets. A flowprocessing facility may facilitate inspecting the content of packetpayloads using content matching, behavioral anomaly detection, acombination of both, and so on.

This application describes another flow processing facility used incomputer security with particular embodiments relating to threats posedby computer viruses. Disclosed herein are various embodiments ofanti-virus methods, systems, techniques and applications, including onesin which ISP provides anti-virus protection to all of its customers viaa flow processing facility.

Provided herein are methods and systems for routing normalized data froma data flow to an antivirus facility for security screening of data flowpatterns, wherein the recognition of patterns is accomplished with theaid of a set of artificial neurons. In embodiments, the networkinterface is to a computer network, an internet, an intranet, a VPN, apersonal computer, a computer resource, a wireless network, or atelecommunications network. In embodiments, the data flow engine isassociated with an application processor module. In embodiments, theapplication processor module includes an antivirus application. Inembodiments, the data flow engine includes a data flow processor. Incertain embodiments, the data flow processor includes a machine learninglogic facility, which may include one or more artificial neurons, suchas using a SOM or a neural network. In embodiments, the data flowprocessor includes a machine learning acceleration hardware. Inembodiments, the data flow processor includes a content search logicfacility. In embodiments, the data flow engine includes a cell generatorand/or a cell router. In embodiments, the cell router is associated withan application processor module. In embodiments, the applicationprocessor module includes or enables an antivirus application. Inembodiments, normalized data is produced using a set of artificialneurons. In embodiments, the set of artificial neurons is associatedwith a data flow processor. In embodiments, the antivirus facility isassociated with, incorporates, or is incorporated in an applicationprocessor module. In embodiments, the antivirus facility is associatedwith a security policy. In embodiments, security screening includesremoval of a virus, quarantining suspect code, sending an alert,triggering a security action (such as updating security policy orconfiguring security hardware) or the like.

The methods and systems disclosed herein include methods and systems forsecuring a computer resource, which include methods in systems forproviding a flow processing facility for processing a data flow, andconfiguring the flow processing facility to recognize patterns in thedata flow, wherein the patterns are relevant to recognition of a threat,such as related to a virus or other threat. In embodiments, the flowprocessing facility recognizes patterns using a set of artificialneurons. In embodiments, the patterns are relevant to recognition of avirus and a spam communication. In embodiments, the patterns arerelevant to recognition of a virus and a hacker's attack. Inembodiments, the patterns are relevant to recognition of a virus andspyware. In embodiments, the patterns are relevant to recognition of avirus and intrusion on a computer network. In embodiments, the patternsare relevant to recognition of a spam communication and a hacker'sattack. In embodiments, the patterns are relevant to recognition of aspam communication and spyware. In embodiments, the patterns arerelevant to recognition of a spam communication and intrusion on acomputer network. In embodiments, the patterns are relevant torecognition of a hacker's attack and spyware. In embodiments, thepatterns are relevant to recognition of a hacker's attack and intrusionon a computer network. In embodiments, the patterns are relevant torecognition of spyware and intrusion on a computer network. Inembodiments, the set of artificial neurons is a self-organizing map or aneural network.

Also provided herein are methods and systems for providing a firewall,including using a flow processing facility that processes a data flow toaddress patterns relevant to a variety of types of threats that relateto computer systems, including computer networks. The flow processingfacility may use a set of artificial neurons for pattern recognition,such as a self-organizing map.

This disclosure describes firewall methods and systems in whichdisparate threat management methods are implemented in a single flowprocessing architecture. In embodiments, the flow processingarchitecture may use a set of artificial neurons, such as a SOM toprocess data flows, wherein the SOM enables recognition of patterns thatare relevant to identifying threats of disparate types, includingthreats relevant to attacks by hackers, network traffic frommalfunctioning computing resources, as well as other types of threats,such as related to unauthorized use of computer network resources.

The methods and systems relating to a firewall disclosed herein includemethods and systems for securing a computer resource, which includemethods in systems for providing a flow processing facility configuredas a flow processing facility to recognize patterns in the data flow,wherein the patterns are relevant to associating the data flow with afirewall application and wherein the flow processing facility recognizespatterns using a set of artificial neurons. In embodiments, the patternsare relevant to recognition of recognition a hacker's attack, amalformation of the dataflow, or a malfunctioning computing resource, orany combination of the foregoing. In embodiments, the set of artificialneurons is a SOM.

This application describes a flow-processing switch used in networkfirewall applications. Firewall applications are described in detail,including an example of a network firewall that provides protectionagainst malformed and non-compliant data packets and malicious attacks.

This application also includes methods and systems for an intrusiondetection and prevention system. An intrusion detection and preventionsystem may include any system or method used to keep attackers fromgaining access to a network, resources on the network, data on thenetwork, or communication pathways into and out of the network. In asimplified form, intrusion detection and prevention may be embodied as afirewall or as anti-virus software. Intrusion detection and preventionmay also provide defense against internal network attacks and helpenforce corporate security policies. Additionally, intrusion detectionand prevention may detect and prevent misuse from authorized users of anetwork by enforcing corporate security policies.

Intrusions, alternatively called attacks, are becoming moresophisticated such that many intrusions are now a blend of attackmethods. Blended attacks may employ a variety of methods (e.g. spam,malware, phishing) simultaneously to compromise security of systems, andspread in a multitude of ways (via e-mail, Web, IM, P2P, even wirelessdevices).

Intrusion detection and prevention may be considered a layered securityinfrastructure that can identify and stop network and application-levelattacks before they inflict any damage by providing detection andprevention capabilities that result in network operational and financialbenefits.

In an aspect of the invention, methods and systems in a flow processingfacility for securing a computer resource may include receiving a dataflow; employing a set of artificial neurons to make a determination, thedetermination indicating which of a plurality of patterns is present inthe data flow; accessing a configuration, the configuration associatingzero or more actions with each pattern of the plurality of patterns;executing the actions that are associated with the patterns that thedetermination indicates, the actions modifying the data flow; andtransmitting the data flow.

In the methods and systems the patterns may be relevant to one or moreof recognition of a virus, a spam communication, a hacker's attack,recognition of a virus, spyware, and intrusion on a computer network.

In the methods and systems, the set of artificial neurons may be aself-organizing map.

In another aspect of the invention, methods and systems in a flowprocessing facility for providing a network service may includereceiving data flow; making a characterization of the data flow, thecharacterization being made by a set of artificial neurons; and routingthe data flow in response to the characterization. In the methods andsystems, the network service may be a security service that may includeone or more of an anti-virus, anti-spam, hacker attack prevention,spyware prevention, intrusion detection, and intrusion prevention.

In the methods and systems, making a characterization may includeinspecting content of the data flow, or analyzing a behavior of the dataflow. The data flow may include data packets. Characterization mayinclude inspecting a payload of the data packets.

In another aspect of the invention, methods and systems of securing acomputer resource may include a flow processing facility for processinga data flow; a configuration facility adapted to configure the flowprocessing facility to recognize patterns in the data flow, wherein therecognition of patterns is accomplished with the aid of a set ofartificial neurons; a receiving facility adapted to receive a data flowfrom a network interface to a data flow engine; a facility adapted tocharacterize from the data flow within the data flow engine; and arouting facility adapted to route the data flow, wherein characterizingthe data flow is achieved with the aid of a set of artificial neuronsand wherein routing is associated with a result of characterizing thedata flow.

In the methods and systems, the network interface may be a wirelessnetwork, a telecommunications network, or a computer network such as aninternet network, an intranet, a VPN, a personal computer, or a computerresource.

In the methods and systems, the data flow engine may be associated withan application processor module. The application processor module mayinclude an antivirus application, or a data flow processor. The dataflow processor may include a machine learning logic facility, a machinelearning accelerator hardware, a search content logic facility. In themethods and systems, the data flow engine may include a cell generator,a cell router that may be associated with an application processormodule that may include an antivirus application.

In another aspect of the invention, methods and systems for securing acomputer resource in a flow processing facility may include receiving adata flow; creating a normalization of the data flow; and routing thenormalization to an antivirus facility. The methods and systems mayfurther include processing the normalized data flow using contentinspection. In the methods and systems, the antivirus facility may beembodied in the flow processing facility. In the methods and systems anormalization of the data flow may include normalizing one or more ofdata packet headers, data packet payloads, protocols, data flowbehaviors, data flow packet arrival time, and data flow packet size.Normalization may be expressed in terms of standard deviations ofmeasurement of features of the data flow, or as a statistical measure ora result of a mathematic calculation. Normalization may also beassociated with neural networks that are applied to the data flow withinthe antivirus facility.

In another aspect of the system, a flow processing facility for securinga computer resource may include a management facility adapted toconfigure the flow processing facility to recognize patterns in a dataflow, wherein the recognition of patterns is accomplished with the aidof a set of artificial neurons; a receive port for receiving the dataflow from a network interface to a data flow engine; a normalizationfacility for producing normalized data from the data flow within thedata flow engine; and a routing facility adapted to route the normalizeddata to an antivirus facility for security screening of data flowpatterns. In the methods and systems, the network interface may be awireless network, a telecommunications network, or a computer networksuch as an internet network, an intranet, a VPN, a personal computer, ora computer resource.

In the methods and systems, the data flow engine may be associated withan application processor module. The application processor module mayinclude an antivirus application, or a data flow processor. The dataflow processor may include a machine learning logic facility, a machinelearning accelerator hardware, a search content logic facility. In themethods and systems, the data flow engine may include a cell generator,a cell router that may be associated with an application processormodule that may include an antivirus application.

In another aspect of the invention, methods and systems in a flowprocessing facility for securing a computer resource, comprising:receiving a data flow; employing a set of artificial neurons to make adetermination, the determination indicating which of a plurality ofpatterns is present in the data flow, the plurality of patterns beingassociated with a firewall application; and routing the data flow to thefirewall application when the determination indicates that at least oneof the plurality of patterns is present in the data flow.

In the methods and systems, the patterns may be relevant to one or moreof recognition of a hacker's attack, a malformation of the data flow,recognition of a malfunctioning computer resource. In the methods andsystems, the anomaly may be associated with the dataflow. In the methodsand systems, the set of artificial neurons may be a self organizing map.

In another aspect of the invention, methods and systems of intrusiondetection and prevention of a network may include detecting an intrusionbased on a signature or a network anomaly; and preventing the intrusionfrom propagating to the network.

In another aspect of the invention, methods and systems of intrusiondetection and prevention in a network may include providing a flowprocessing facility in-line with a network firewall; configuring theflow processing facility to detect intrusions that pass through thefirewall; routing the detected intrusions to a prevention processor; andtaking a preventive action on the detected intrusion such that the dataflow of the detected intrusion is not propagated to the network.

In another aspect of the invention, methods and systems of intrusiondetection and prevention of a network comprising: flow processingfacility that is configured to detect and prevent intrusions in networkdata flowing through the facility, the facility comprising a pluralityof network ports for connecting network devices for communicatingnetwork data; and a data flow processor for associating network dataflows with one or more of signatures, process anomaly thresholds,network rate thresholds.

In another aspect of the invention, methods and systems may include aflow processing facility for processing a data flow; a facility adaptedto configure the flow processing facility to recognize patterns in thedata flow; a receiving facility adapted to receive the data flow in adata flow processor facility; and a facility adapted to producenormalized data based at least in part on at least one of a plurality ofmachine learning logic associated with the data flow processor facility.

In the methods and systems, the data flow may be associated with anetwork interface such as a wireless network, a telecommunicationsnetwork, and a computer network. The computer network may be an internetnetwork, an internet, a VPN, a personal computer, or a computerresource. In the methods and systems the data flow processor may includea machine learning logic facility, a machine learning accelerationhardware, or a content search logic facility.

In the methods and systems, may be at least one of computer code,computer file type, software application type, virus, a spamcommunication, a hacker's attack, spyware, and intrusion on a computernetwork and wherein the flow processing facility recognizes patternsusing a set of artificial neurons.

In the methods and systems, the normalized data may be produced using aset of artificial neurons or at least one of a set of self organizingmaps. The artificial neurons may be associated with a data flowprocessor.

In another aspect of the invention, methods and systems may include aflow processing facility for processing a data flow, wherein the dataflow comprises packets; a plurality of packets, wherein each packetincludes a payload; an application processing module of the flowprocessing facility for inspecting a content of the payload of at leastsome of the plurality of packets; and a switch matrix for controllingthe flow of packets related to the inspected packets based on theinspection.

In the methods and systems, content inspection may include contentmatching which may include regular expression matching. Contentinspection may include using one or more of self organizing maps, usingneural networks, using behavioral anomaly detection. Behavioral anomalydetection may include neural networks, or self-organizing maps. Contentinspection may be based on one or more action rules or a securitypolicy.

In the methods and systems, content inspection may determine the natureof data in the packet payload. The nature may include one or more of asource of the packet and a data type of the packet. The source mayinclude a website, while the data type may include one or more of audio,video, email, and program executable code. In the methods and systems,the packet may be associated with a layer of a communication protocolsuch as a network layer, an application layer, and a transport layer.

In another aspect of the invention, methods and systems may includeproviding a flow processing facility for processing a data flow, whereinthe data flow comprises packets; receiving a stream of packets, whereineach packet includes a payload; determining the nature of data in apayload of a first packet; determining the nature of data in a payloadof a second packet; comparing the first packet nature to the secondpacket nature; controlling a flow of the stream based on the comparison.

In the methods and systems, controlling may include marking the packetsof the stream, rejecting packets of the stream, redirecting the streamto a secure process, or redirecting the stream to an inspection processwith in the flow processing facility.

In another aspect of the invention, methods and systems may includeproviding a flow processing facility for processing a data flow;determining a behavioral time-history metric of a portion of the dataflow; determining a behavioral metric of a current packet related to theportion; comparing the behavioral metric to the time-history metric; andcontrolling a flow of packets related to the current packet based on thecomparison.

In the methods and systems, the time-history metric may be associatedwith a layer of a communication protocol. In the methods and systems,the portion of the data flow may be related to synchronizing a mobilecomputing device with a network resource.

In an aspect of the invention, methods and systems may include a networkinterface for receiving packets; a processor for executing contentinspection algorithms; and a network processing module for directing thepackets based on a result of the processor executing the contentinspection algorithms on the received packets.

In the methods and systems, the flow processing facility may be embodiedas a network appliance, a network firewall, or a computer program. Thefirewall may be embodied as a computer program or a network appliance.

In the methods and systems, the processor may be a COTS processor. Thealgorithms may be compiled to a native format compatible with the COTSprocessor, and wherein the compiled algorithms are stored in a memoryaccessible by the processor. In the methods and systems, the processormay be a special purpose processor, and wherein the algorithms areconfigured in hardware elements of the processor. The special purposeprocessor may be an application accelerator. The methods and systems mayfurther include an application accelerator for accelerating processingof the packets.

In another aspect of the system, a methods and systems of a flowprocessing facility may include a plurality of application processormodules for detecting intrusions in packet payloads, wherein each of theplurality of application processor modules is configured to detectintrusions at a specific network layer; and a switching fabric forrouting packets through the plurality of application processor modulesso that a packet is processed through at least two processor modules.

In the methods and systems the packet may be replicated by the switchingfabric for parallel processing in at least two processor modules.Alternatively, a packet may be processed through a first processormodule and then processed through a second processor module of theplurality of processor modules.

In another aspect of the invention, methods and systems of networksecurity may include providing a flow processing facility for processinga data flow, wherein the data flow comprises communication packets;receiving the communication packets, wherein each packet comprises aplurality of protocol layer packet data; processing the packets todetermine a corresponding protocol layer for each packet data; andinspecting each packet data according to the corresponding protocollayer inspection rules.

In another aspect of the invention, methods and systems of a firewallfacility may include a flow processor for processing network packetsbeing transferred between an intranet port and an extranet port of thefirewall; content inspection algorithms executed by the flow processingfacility to detect abnormalities in the packets; content strings thatdefine invalid packets; and an application processing module fordetermining if an abnormal packet is an invalid packet. The methods andsystems may include a network processing module for taking action onabnormal or invalid packets. Taking action may include dropping thepackets or dropping subsequent packets associated with a stream of theabnormal or invalid packets. In the methods and systems, the contentstrings may define an invalid application layer packet header, aninvalid network layer packet payload, malicious code, one or morecomputer viruses, or one or more spam campaign packets. The contentinspection algorithms may include one or more of behavioral analysis andregular expression matching.

In another aspect of the invention, methods and systems may includeproviding a flow processing facility for processing a data flow, whereinthe data flow comprises routed data packets; providing routinginformation for the data packets; inspecting the packets to determine avalidity for each packet; combining the inspection result with packetrouting information into a network behavior; establishing a baseline fornetwork behavior; and comparing ongoing network behavior to the baselineto detect abnormal network behavior in the flow processing facility. Thepacket routing information may include one or more of a port identifier,a source, a destination, and a route.

In another aspect of the invention, methods and systems of virtualnetwork security may include providing a flow processing facility forprocessing a data flow; establishing a first security policy for a firstvirtual network; establishing a second security policy for a secondvirtual network; and processing the data flow for the first and secondvirtual networks through the data flow processor, wherein portions ofthe data flow that are associated with the first virtual network areprocessed according to the first security policy, and wherein portionsof the dataflow that are associated with the second virtual network areprocessed according to the second security policy. The data flow may becomposed of data packets. The portions of the data flow associated withthe first virtual network may include the data packets associated withthe first virtual network, and wherein the portions of the data flowassociated with the second virtual network comprise the data packetsassociated with the second virtual network.

In the methods and systems, each virtual network may support one or moreof an enterprise, individual user, home user, home office user, serviceprovider, security provider, central office, remote office, dataprovider, university, social club, public facility, library, townoffices, state offices, federal offices, and virtual private network.Each security policy may support one or more of unified threatmanagement, intrusion detection, intrusion prevention, intrusiondetection and prevention, internet firewall, URL filtering, anti-virus,anti-spam, anti-spyware, http scanning, application firewall, xmlfirewall, and vulnerability scanning.

In another aspect of the invention, methods and systems of a virtualnetwork security service may include providing a flow processingfacility for processing a data flow; establishing a virtual network fora customer; receiving a security policy for the customer receiving adata flow including data packets that are associated with at least thecustomer; routing the data flow through the flow processing facility;and applying the security policy to data packets that are associatedwith the customer.

In another aspect of the invention, methods and systems of a virtualinternet firewall may include a flow processing facility for processinga data flow; a security policy of a first virtual network; a securitypolicy of a second virtual network; and routing the data flow throughthe firewall so that the flow processing facility processes the dataflow according to the first security policy and according to the secondsecurity policy. The methods and systems may further include amanagement facility. The management facility may update one of thesecurity policy of the first virtual network and the security policy ofthe second virtual network.

In another aspect of the invention, methods and systems may includeproviding a plurality of flow processing facilities for processing adata flow; providing a network management facility that is networkedwith the plurality of flow processing facilities; configuring two ormore of the plurality of flow processing facilities into a virtualnetwork; and managing a security policy of the virtual network, whereinthe two or more flow processing facilities in the virtual networkreceive and execute the security policy.

In the methods and systems, managing may include updating two or moreflow processing facilities simultaneously. In the methods and systems,each of the two or more flow processing facilities may be connected todifferent network segments. At least one of the two or more flowprocessing facilities may be located remotely from the others of the twoor more flow processing facilities. Being remotely located may includebeing connected through the internet.

The methods and system may further include routing portions of the dataflow through a switch fabric to each of the two or more flow processingfacilities.

In another aspect of the invention, methods and systems of testingnetwork security may include providing a flow processing facility forprocessing a data flow; providing two virtual networks; configuring theflow processing facility to process the data flow through each of thetwo virtual networks in parallel; applying an experimental securitypolicy to one of the two virtual networks; and comparing the processingof the data flow through the two virtual networks to test theexperimental network security policy.

In another aspect of the invention, methods and systems of networksecurity may include providing a flow processing facility for processinga data flow, wherein the data flow processing facility includes amemory; receiving a data flow into the memory; assembling the data flowinto data streams; processing the data stream in the data flowprocessing facility for detecting network security violations; andpreventing the data stream from propagating the security violations tothe network.

In the methods and systems the flow processing facility may be embodiedas a firewall. The firewall may be embodied as a network appliance. Theflow processing facility may be embodied as a program executing on anetworked computing facility. Security violations may include intrusionof applications, databases, file systems, operating systems, networkcommunications, and security policies. Detecting may include analyzingsystem calls, application logs, file-system modifications, serveractivities, and server states. The networked computing facility may be anetwork server, a web server, a management server, a client computer, ahub, or a router.

In the methods and systems, detecting may include one or more of packetheader inspection, packet payload inspection, content inspection, datastream behavioral anomaly detection, content matching, regularexpressing matching, self-organizing maps, misuse algorithms, networkprotocol analysis, and neural networks. Preventing may includequarantining, dropping packets, dropping a data stream, re-routingpackets, re-routing a data stream, URL filtering.

In the methods and systems, receiving may include network transmissionfrom one or more of a firewall, network appliance, network server,network client, a virtual private network, a wireless network, networkrouters, network hubs, network segments, VoIP ports, users, and webclients.

In the methods and systems, data flow may be comprised of data packets.In these methods and systems, processing may include inspecting one ormore of data packet headers, data packet payloads, network layerpackets, application layer packets, and transport layer packets.

In another aspect of the invention, methods and systems of networksecurity may include providing a flow processing facility for processinga data flow; receiving a network activity baseline; processing a dataflow to calculate a metric of network activity; and comparing thebaseline to the metric to detect one or more anomalies in the data flow;preventing an anomalous data flow from propagating an intrusion to thenetwork. Comparing may include protocol analysis which may include lowlevel analysis of the data flow such as analysis of network layer andtransport layer protocols. Protocol analysis may alternatively includeone or more of packet arrival time stamping, packet filtering, andpacket triggering. Arrival time stamping may facilitate detectinganomalies in two or more data flows that are merged together. The metricmay include a rate of network activity.

In another aspect of the invention, method and systems of networksecurity may include providing a flow processing facility for processinga data flow; learning a network activity baseline; processing a dataflow to calculate a rate of network activity; comparing the learnedbaseline to the calculated rate to detect one or more anomalies in thedata flow; and preventing an anomalous data flow from propagating anintrusion to the network.

In the methods and systems, learning may include using self-organizingmaps, using neural net algorithms, or predicting a rate of networkactivity. The predicted rate may include estimating one or more of totalnumber of data packets, number of IP packets, number of ARP packets,connections/second rate; data packets/connection rate, number of datapackets per port.

Methods and systems may further include adjusting the activity baselinebased on an aspect of the network.

In the methods and systems, the aspect may be past network activityrates.

In the methods and systems, preventing may include one or more ofgranular rate-limiting on a specific dimension of an intrusion, sourcetracking, connection tracking, dark-address filtering, network scanfiltering, port scan filtering, legitimate IP address validation, datapacket rejection, data stream rejection, alerting, anomaly logging, andrerouting a data stream to a virtual network. Alerting may include oneor more of email notification, system logging, snmp output, SMS-externaltransmission, calling a pager, executing an application, spawning aprocess, console updating, and instant messaging.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a networked computing environment.

FIG. 2 depicts an expanded view of a flow processing facility in thenetworked computing environment.

FIG. 3 depicts an expanded, logical view of a network processor module.

FIG. 4 depicts an expanded, logical view and process flow of a data flowengine.

FIG. 5 depicts an expanded, logical view of an application processormodule.

FIG. 6 depicts a logical progression for reconfiguring the flowprocessing facility in response to a changing data flow.

FIG. 7 depicts an expanded, logical view and process flow of contentsearching.

FIG. 8 depicts an expanded, logical view and process flow of portions ofFIG. 7.

FIG. 9 depicts a pattern tree for using an initial root node.

FIG. 10 depicts a pattern tree with failure transitions of terminalnodes.

FIG. 11 depicts details of a pattern tree with a terminal node and anempty leaf node.

FIG. 12 depicts a pattern tree with a terminal node and an empty leafnode.

FIG. 13 depicts a pattern tree embodied in a computer memory.

FIG. 14 depicts a relationship of pattern position tables.

FIG. 15 depicts a pattern tree resulting from using an initial rootnode.

FIG. 16 depicts a pattern tree resulting from using an initial root nodewith an alternate data expression.

FIG. 17 depicts a pattern tree resulting from using an initial root nodewith another alternate data expression.

FIG. 18 depicts the use of a Header-ID.

FIG. 19 depicts an address bit map.

FIG. 20 depicts a two-packet replay.

FIG. 21 depicts a simplified communication processing system.

FIG. 22 depicts a simplified arrangement of a behavior analysis engine.

FIG. 23 depicts a neural net.

FIG. 24 depicts a learning phase of a neural net.

FIG. 25 depicts a detection phase of a neural net.

FIG. 26 depicts an expanded, logical view and process flow of a neuron.

FIG. 27 depicts real-time updates to a self-organizing map.

FIG. 28 depicts reduction of false positives.

FIG. 29 depicts a computer network incorporating intrusion detection andprevention.

FIG. 30 depicts a simplified schematic of an enterprise network.

FIG. 31 depicts the relationship of packet payloads to IP stack layers.

DETAILED DESCRIPTION

An aspect of the present invention involves systems and methods forprocessing data flows. This data flow processing includes deployingsoftware and/or hardware applications in a networked computingenvironment 100, wherein the applications operate within a networkcomponent referred to hereinafter as a flow processing facility 102. Itwill be appreciated that the flow processing facility 102 may indeedinclude a networking switch. However, it will also be appreciated thatthe flow processing facility 102 need not be a networking switch, butinstead another type of network computing device. All such embodimentsof the flow processing facility 102, many of which are described indetail hereinafter and others of which will be appreciated from thepresent disclosure, are intended to fall within the scope of the presentinvention.

Aspects of the present invention may relate to and/or be directed atand/or associated with one or more of the following networkapplications: firewall; intrusion detection system (IDS); intrusionprotection system (IPS); application-level content inspection; networkbehavioral analysis (NBA); network behavioral anomaly detection (NBAD);extrusion detection and prevention (EDP); any and all combinations ofthe foregoing; and so forth. Additionally or alternatively, aspects ofthe present invention may provide and/or be associated with a securityevent information management system (SEIM); a network management system(NMS); both a SEIM and a NMS; and so on. The network applications mayexist and/or be associated with a network computing environment, whichmay encompass one or more computers (such as and without limitation theserver computing facilities) that are operatively coupled themselvesand/or to one or more other computers via a data communication system.Many data communications systems will be appreciated, such as aninternetwork, a LAN, a WAN, a MAN, a VLAN, and so on. In embodiments,the communications system may comprise a flow processing facility. Theflow processing facility, an object of the present invention, mayprovide, enable, or be associated with any and all of the aforementionednetwork applications. Additionally or alternatively, the flow processingfacility may provide, enable, or be associated with numerous otherfunctions, features, systems, methods, and the like that may bedescribed herein and elsewhere.

Any and all of the network applications, the SEIM, the NMS, and so forthmay comprise a facility or group of facilities that may be implementedas one or more software programs and/or hardware devices. Inembodiments, these facilities may be integrated into a networkedenvironment and may function within that networked environment.

The firewall may implement one or more measures to detect, prohibit,circumscribe, and/or otherwise limit packet-based, logical connectionsand individual network packets that are disallowed, such as and withoutlimitation by a reference network security policy. Such a policy mayconsist of information concerning the conditions (if any) under which afacility that is interacting with a network may be granted access toand/or from network resources, facilities, services, devices, and thelike.

The firewall may operate on packets of a data flow. In embodiments, thefirewall may process the headers of the packets, the payloads of thepackets, or both. The firewall may embody a stateful process thatexamines the headers, payloads, or both in the context of a networkstate. This state may relate to a session or connection that isassociated with a particular protocol or application in use over thenetwork. In an example and without limitation, this state may relate toa TCP/IP connection.

While the firewall may be an example of an intrusion detection system,the intrusion detection system may implement one or more measures todetect unwanted manipulations of a networked resource (such as a networkfile or network file system, a server facility, a desktop computingfacility, a networked printer, and so on). Such manipulations may,without limitation, comprise accessing, modifying, deleting, utilizing,denying service, activating service, hiding, revealing, naming,renaming, logging in, logging out, and so on.

In embodiments, the intrusion detection system may be directed atdetecting intrusions by examining, monitoring, or otherwise processinginformation associated with a network protocol, a communicationstechnique, a computing application, a business method, and so on. Suchprocessing may be related to data packets, communications flows of datapackets, trends in communications flows, and so on. The intrusion mayoperate in a passive manner (simply observing data packets and relatedflows) or in an active or reactive manner (by participating in acommunication, such as and without limitation by intercepting,generating, modifying, or otherwise affecting data packets and relatedflows). The intrusion detection system may itself provide one or morenetworked resources, such as and without limitation a honeypot, whichmay entice a would-be intruder to interact directly with the intrusiondetection system and, thus, be detected by the system. It will beappreciated that the intruder may be a human user, an automatic process,some combination of the two, or a plurality of the foregoing. It will beappreciated that the intrusions may relate to an intentional misuse of anetwork resource, an unintentional or erroneous misuse of a networkresource (such as due to a process error in a computer program), and soon.

The intrusion prevention system may implement one or more measures toprevent unwanted manipulations of a networked resource. In other words,the intrusion prevention system may be related to the intrusiondetection system, but may be directed at preventing intrusions ratherthan simply detecting them. In fact, these systems may be so closelyrelated that detection and prevention capabilities can be combined intoan intrusion detection and prevention system. Generally, such a systemmay monitor for unwanted manipulations of networked resources and eitherprevent them entirely or stop them while they are still in progress. Inan example and without limitation, a networked resource may come under adenial of service attack, in which the resource is flooded withmalicious data packets. If the data packets are well-formed and wouldotherwise not represent an attack were they present in fewer numbers,the intrusion detection and prevention system may (perhaps onlymomentarily) not recognize the onset of the attack. In this case, theintrusion detection and prevention system may stop the attack while itis in progress. If, however, the data packets are malformed or otherwisesuspect, then the system may be able to recognize even the firstinstance of these packets and prevent the attack entirely.

Application-level content inspection may relate to processing a dataflow by examining the application-layer payloads of the packets thatmake up the flow. Such processing may be aware of application-levellogic and/or the measured or expected communication patterns of anapplication. Such “awareness” may be provided by a program (orprogrammed logic) and/or may be acquired over time, such as and withoutlimitation according to an artificial intelligence system or method.Application-level content inspection, perhaps like all data flowprocessing, may consist of pattern matching, behavioral analysis,anomaly detection, and so forth. It will be appreciated thatapplication-level content inspection may be an aspect of any and allsystems and methods that are directed at or responsive to theapplication-layer information of a data flow. It will be appreciatedthat the application layer may reside above the transport layer, networklayer, and data link layer in the IP protocol stack. It will also beappreciated that the application layer may reside above all other layersin an OSI protocol stack.

Network behavioral anomaly detection may monitor network data flows soas to detect anomalous data flows. Such flows may contain types,patterns, frequencies, or other aspects of data that are unusual,unexpected, new, different, or otherwise unlike a normal flow. Suchterms as “normal” and “anomalous” may be inherently broad because whatis normal for a particular network environment (of servers, clients,network connections, network devices, and so on) may be anomalous foranother. In an example and without limitation, a network environmentcontaining a file server may exhibit a relatively large amount of dataflows out of the file server as other computing devices access files onthe server. However, a network environment that doesn't contain a fileserver may exhibit relatively large amounts of data flowing out of aserver only under anomalous conditions, such as and without limitationwhen a particular server contains a malicious program that illicitlytransmits files from the server to another computing facility. Manyother such examples of normal and anomalous data flows will beappreciated and all such examples are within the scope of the presentinvention. In any case, behavioral anomaly detection may encompassartificial intelligence or machine learning techniques that allowsoftware programs and/or hardware devices to obtain a model of what dataflows are “normal,” perhaps (in whole or in part) by observing dataflows in the networked environment. Then, by comparing actual data flowswith such a model, it may be possible to detect anomalies. The observingand comparing of data flows may, without limitation, include processingheaders, payloads, protocols, and so on. In embodiments, this processingmay comprise regular expression matching on payloads and/or protocols.

Extrusion detection and prevention may detect and prevent thetransmission (“act of extrusion”) of classified, secret, sensitive,protected, confidential, proprietary, or otherwise private informationfrom within an authorized network area out to an unauthorized networkarea. A network area may comprise a LAN, MAN, WAN, VLAN, or any and allother instances of a data network. A network area may exist withinanother network area, such as a VPN may be used to establish a privatenetwork within an otherwise public network (or, for that matter, may beused to establish a private network within an already private network).In embodiments and without limitation, a network may comprise a singlecomputing facility, such as a network server. In this case, thetransmission of private information from the network server, regardlessof the destination, may be considered an act of extrusion. Inembodiments and without limitation, a network may comprise any number ofcomputing facilities, such as network servers, switches, routers, hubs,clients, and the like. In any case, the information may comprise a filesystem, a database, a file, a record, a field, a value, a sequence ofbytes, a byte, a bite, or any and all information. Thus, extrusiondetection and prevention may examine whether and/or what traffic flowsto and/or from a particular network area; the content of the traffic;and so on. In an example and without limitation, a corporation maycontrol a network area. The corporation, as a general privacy policy,may not want social security numbers to be transmitted from its networkarea out to other network areas. An embodiment of extrusion detectionand prevention may be able to enforce such a policy by blocking any andall data flows that contain (or, at least, appear to contain) a socialsecurity number from being transmitted out of the network area toanother network area (such as and without limitation the Internet). Manyother such examples will be appreciated and all such examples are withinthe scope of the present invention.

Security event information management systems and methods may processsecurity event information. Security event information may encompass anyand all information that may be generated during the course ofprocessing, monitoring, blocking, allowing, modifying, routing,rerouting, or otherwise handling or observing any and all aspects of anyand all data flows that are associated with the networked environment.The processing of security event information may be directed atcollecting, storing, monitoring, and/or otherwise processing thesecurity event information. In embodiments, this information may takethe form of alerts, logs, emails, text messages, signals, instantmessages, or any and all other forms of system, automatic, or manualmessages. The processing of the security event information may includeresponding to particular security event information, perhaps inaccordance with a risk factor that may be associated with theinformation. In an example and without limitation, security eventinformation that is indicative of a major network security breach may beassociated with a high risk factor. The processing of the security eventinformation may include producing a report of the responses to theparticular security event information. This report may comprise an audittrail, which may allow an auditor to view a history of events,associated risk factors, association actions taken in response, and soforth.

Network management systems and methods may monitor any and allperformance metrics that may be associated with a networked computingenvironment and, perhaps in response to this monitoring, may adjust anyand all parameters or aspects of the networked computing environment sothat the performance metrics are returned to and/or maintained atpredetermined, estimated, calculated, or otherwise specified levels.These systems and methods may address one or more aspects of a networkmanagement model, such as and without limitation performance management,configuration management, accounting management, fault management,security management, and so forth.

Configuration management may encompass the monitoring versions ofsoftware, firmware, hardware, and the like that are associated with thenetworked computing environment. Configuration management may bedirected at monitoring (and, if need be, adjusting) any and all aspectsof the networked computing environment's performance in light of theseversions. As the versions themselves may be an aspect of the networkedcomputing environment, configuration management may adjust the versions,such as and without limitation by requesting or automatically conductingthe installation of software, firmware, hardware, and the like.

Fault management may encompass an automatic detection of a fault in anetworked computing environment and an automatic action directed atcorrecting the fault. This automatic action may comprise transmitting analert to a human operator of the networked computing environment,automatically reconfiguring or adjusting any and all aspects of thenetworked computing environment, and so forth. In embodiments andwithout limitation, the fault may comprise a link failure, a nodefailure, a power failure, intermittent communications, degradedcommunications, and the like.

Security management may encompass the monitoring and/or control ofaccess to resources in a networked computing environment. Access toresources in a networked computing environment may, without limitation,comprise logging into a resource, communicating with a networkedresource, configuring or reconfiguring a networked resource, monitoringa networked resource, and so forth. Security management may include theautomatic logging of any and all access (or attempted access) to any andall resources in the networked computing environment. The control ofaccess to the resources may consist of partitioning the networkedcomputing environment into authorized areas and unauthorized area.Authorization may encompass a mapping of network users to networkresources, wherein the mapping indicates whether a particular user hasaccess to a particular network resource (and, if so, under whatconditions, if any). Security management may include the automaticgeneration of such mappings.

It will be appreciated that any and all of the aforementioned features,functions, systems, and methods may be combined, according to thepresent invention, with machine learning or artificial intelligencetechniques (as described hereinafter and elsewhere) into a singlefacility. In an example and without limitation, NBA or NBAD may becombined with a SOM into a single facility. In this way, the SOM (or,more generally, artificial neurons) may store historical information,perhaps obviating the use of a database to store the historicalinformation. In any case, in embodiments, this single facility mayencompass a general purpose host processor, such as and withoutlimitation a COTS CPU. Many other such examples are describedhereinafter and still others will be appreciated. All such examples arewithin the scope of the present invention.

Referring to FIG. 1, an example networked computing environment 100 fordata flow processing includes a flow processing facility 102 that isoperatively coupled to an internetwork 104, a plurality of servercomputing facilities 108, and a number of departmental computingfacilities 110 that are associated with an enterprise. In the depiction,the departmental computing facilities 110 are an engineering department(Eng Dept.) a marketing department (Mktg Dept.) and another department(Other Dept.). The flow processing facility 102 is described in detailhereinafter with references to FIG. 1, FIG. 2, and other figures.Generally speaking, the flow processing facility 102 receives,processes, and transmits a data flow, which is described in detailhereinafter with reference to FIG. 4. The internetwork 104 may be theInternet, or it may be any wired, wireless, or combined wired/wirelessdata network for transmitting flows of data between one computingfacility and another.

The networked computing environment 100 also includes a plurality ofnetwork-connected computing facility 112. These facilities 112 may ormay not be associated with the enterprise. The network-connectedcomputing facilities 112 may include any client or server computingdevice that may be operatively coupled to the internetwork 104. Thesefacilities 112 may be provided in the present depiction to illustratethat any number of a variety of computing devices may be operativelycoupled to the internetwork 104. Via the internetwork 104, thesefacilities may communicate data flows with the flow processing facility102. Via the flow processing facility 102 and the internetwork 104, thenetwork-connected computing facilities 112 may communicate with one ormore of the server computing facilities 108 or any of the departmentalcomputing facilities 110.

The server computing facilities 108 may receive and transmit data flows.These flows may be directed at the departmental computing facilities 110or they may be directed at a computing facility that is operativelycoupled to the internetwork 104. In any case, the flow processingfacility 102 receives data flows from the departmental computingfacilities 110 and other computing facilities via the internetwork 104.The flow processing facility 102 may classify, categorize, or otherwiseprocess the data flows. Depending upon this processing, the flowprocessing facility 102 may: discard some or all of the data flow;modify some or all of the data flow; pass through some or all of thedata flow in an unmodified form; analyze some or all of the data flow;and so forth. Additionally, the flow processing facility 102 may performas a network switch, hub, router, server, client, gateway, proxy,reverse proxy, load balancer, server, Web server, application server,firewall, URL filter, VLAN, or any other network, data flow, packethandling, or application-level service. Many such services are describedin detail hereinafter and still others will be appreciated from thisdisclosure. All such services are encompassed by the present inventionand are intended to fall within the scope thereof.

In the preferred embodiment, flows of data are implemented as a set ofassociated Internet Protocol (IP) packets. However, it will beappreciated that all possible embodiments of flows of data over theinternetwork 104 may be transmitted, received, and processed by the flowprocessing facility 102. Generally, the flow processing facility 102 isadaptable to any network environment utilizing any network protocols.The flow processing facility 102 may support literally any link-, data-,transmission-, or application-level protocol. It will be seen that thisadaptation is achieved through a variety of software or hardwarefeatures, all of which are subjects of the present invention.

Those skilled in the art will appreciate that the example networkedcomputing environment 100 is simplified for pedagogical purposes. In anexample, the environment 100 does not show the plurality of networkingdevices that comprise the internetwork 104, the various hubs, routers,and switches that may comprise the networked computing operation of anactual enterprise, and so on. These simplifications are provided for thepurpose of drawing attention to the flow processing facility 102, whichis an object of the present invention. However, given that networkedcomputing environments 100 can be arbitrarily complex and assume acountless number of configurations, the deployment of the flowprocessing facility 102 is not in any way limited to the particularnetworked computing environment 100 shown here. Generally, the flowprocessing facility 102 can provide a service even when only one othercomputing device is operatively coupled to it. While particularembodiments of the flow processing facility 102 may be limited in thenumber of physical, operative couplings that are supported (such as dueto a limited number of physical network ports), there is no theoreticallimit to the number of physical, operative couplings that could besupported by a flow processing facility 102. Moreover, the flowprocessing facility 102 does not inherently limit the number of logicaloperative couplings (such as and without limitation, TCP/IP connections)that can be present in embodiments. Many more advantages, features, andfunctions of the flow processing facility 102 are described hereinafterand elsewhere.

In embodiments, the flow processing facility 102 may be deployed indedicated network hardware; associated with dedicated network hardware;contained in or by dedicated network hardware; connected with dedicatednetwork hardware; and so forth. In embodiments, the flow processingfacility 102 may contain, comprise, include or encompass dedicatednetwork hardware. This dedicated hardware may, without limitation, beprovided in a rack-mount unit, in a chassis/blade configuration, or in astandalone unit with an arbitrary form factor. The standalone unit maybe a consumer-oriented device, comprising without limitation one or moreof a firewall, a router, a wireless access point, a print server, anHTTP management interface, an Ethernet port, a URL filter, and a MACaccess control list.

In embodiments, the flow processing facility 102 can be deployed in,associated with, or comprise a shared device that supports theflow-processing features of the present invention and additionalfeatures. This shared device can be a network client, such as andwithout limitation a PC, cell phone, pager, laptop, PDA, networkedsensor, set-top box, video game console, TiVo, printer, VoIP device,handheld computer, smart phone, wireless e-mail device, Treo,Blackberry, media center, XBOX, PlayStation, GameCube, palmtop computer,tablet computer, and the like. The shared device can be a networkserver, such as and without limitation a rack mount computer, bladecomputer, tower computer, supercomputer, quantum computer, and so forth.The shared device can be an application server, such as and withoutlimitation a database server, Web server, file server, print server, Webservices server, e-mail server, calendar server, software update server,e-commerce server, blog server, proxy server, reverse proxy server, andso forth. The shared device can be a network component, such as andwithout limitation a router, switch, hub, IP gateway, VoIP gateway, SAN,NAS, modem, wireless access point, firewall, load balancer, cable modem,DSL modem, satellite modem, DSLAM, NIC, and the like.

In embodiments, the flow processing facility 102 can be deployed in,associated with, or comprise an auxiliary device that supports theflow-processing features of the present invention and any and alladditional features. This auxiliary device can, without limitation, be adongle, USB key, FireWire device, smart card, securID, Disk-On-Chip, andso forth.

In embodiments, the flow processing facility 102 can be deployed in,associated with, or comprise network software that supports theflow-processing features of the present invention and additionalfeatures. This software can be dedicated software, such as and withoutlimitation a standalone application, a server application, anapplication suite, and so forth. The software can be shared localsoftware such as and without limitation a library, a library function ormethod, an embedded operating system, and so forth. The software can beshared, networked software such as and without limitation a Web serviceor the like. Other kinds of network software will be appreciated fromthis disclosure and such network software is intended to be encompassedby the present invention.

In embodiments, the flow processing facility 102 may comprise avirtualization aspect. This aspect may cut across any and all of thesystems and methods described herein, so as to support thevirtualization of them. In embodiments, virtualization may be applied toprovide a logical arrangement of policies, networks, behavioralanalyses, applications, any and all combinations of the foregoing, andso on. Virtualization may enable the flow processing facility 102 toprovide its features and functions in ways that are logically beneficialor convenient; logically tailored to data flows or to users of dataflows; consistent with an abstract and logical model (as opposed to aliteral and physical model); and so forth. In an example and withoutlimitation, virtualization may present the server computing facility 108with different policies, networks, behavioral analyses, applications,and so on than it provides to the network-connected computing facility112. From the perspective of the server computing facility 108 and thenetwork-connected computing facility 112, there may not be an indicationthat virtualization is in effect. In other words, the flow processingfacility 102 may subject the server computing facility 108 to policies,networks, behavioral analyses, applications, and so on withoutindicating that those are being provided according to virtualization andwithout providing any indication as to whether other network resources(such as and without limitation the server computing facilities 108) arebeing subject to the same. Other such applications of virtualization maybe described herein and still others will be appreciated. All suchapplications of virtualization are within the scope of the presentinvention.

Referring now to FIG. 2, another view of the example networked computingenvironment 100 is shown. Here the view of the flow processing facility102 is expanded while the rest of the environment 100 is contracted. Thepublic network 202 may encompass the internetwork 104 or any other datacommunications network, whether wired, wireless, packet-oriented,digital, analog, and so forth. The private network 204 may encompass anydata communications network and may include the server computingfacilities 108, the departmental computing facilities 110, and so forth.

A management server 228 is associated with the flow processing facility102. The management server 228 provides an administrative interface tothe flow processing facility 102. Via this interface, any function orfeature of the flow processing facility 102 may be configured, edited,monitored, modified, installed, uninstalled, activated, deactivated, orotherwise controlled or viewed by an administrator. The managementserver 228 may be composed of a computer or computing facility thatprovides a user interface to better enable interaction with theadministrator. The management server 228 is operatively coupled with theflow processing facility 102 via a data network. In some embodiments,this data network may encompass a dedicated physical data connectionbetween the management server 228 and the flow processing facility 102,such as may be provided by a serial cable, an Ethernet cable, a wirelesscommunication channel, or any other such device. The management server228 may provide a graphical user interface, which can be interactive(i.e. both providing information to and accepting information from theadministrator) or can be non-interactive (i.e. simply providinginformation to the administrator). Alternatively, the management server228 may provide a command-line interface, which may accept textualcommands that are input by the administrator and in return providetextual responses to those commands. In some embodiments, a graphicaluser interface is provided that also includes a window containing acommand-line interface.

The graphical user interface (GUI) or command-line interface (CLI) isprovided for configuring and monitoring the flow processing facility 102and its elements. The management server 228 renders this interface andaccepts input associated with the interface. Communication between themanagement server 228 and the control processor module 208 enables theuser interface by transmitting instructions from the user interface tothe flow processing facility 102 and by transmitting information fromthe flow processing facility 102 to the user interface. Thecommunication between the management server 228 and the controlprocessor is conducted over an out-of-band data network that is not thedata network 202 or 204 that provides the data flows. Data packets,which may be described in greater detail hereinafter with reference toFIG. 4, may be associated with data flows and are subject to processingby an application processor module 212.

The GUI/CLI may be provided by an administration application that isinstalled on the management server 228 by an installation wizard. Theadministration application may utilize SNMP to securely retrievestatistics and trap information from the flow processing facility 102.All communications between the GUI/CLI application and the flowprocessing facility 102 may be secured according to SSH, SSL, HTTPS, orany other secure data communication protocol. An audit trail, which canbe maintained by both the flow processing facility 102 and theadministration application, may contain any or all informationpertaining to communications between the management application, theperformance or actions of the flow processing facility 102 and itselements and so forth. The administration application may be a nativemanagement tool associated with an application that is provided by theapplication processor module 212. In this way, although the applicationresides in the application processor module 212, an administrator canmanage the application as though it were installed in a traditionalserver such as the Dell PowerEdge 850 server, and not in aflow-processing switch according to the present invention.

Administration of the flow processing facility 102 and its elements canbe provided via a three-tiered, role-based administration technique. Amaster administrative role may be associated with complete access to theelements of the system. This role may also be associated with theability to create a plurality of sub-administrators. With access rightsto specific devices or device groups, the sub-administrator role may beassociated with a number of privileges.

The flow processing facility 102 is implemented according to anarchitecture, which, in the preferred embodiment, may comprise a switcharchitecture. This architecture may include a network processor module210, an application processor module 212, and a control processor module208. The network processor module 210 may be described in detailhereinafter with reference to FIG. 3. In the preferred embodiment, eachof the processor modules 208, 210, 212 are adapted to physically coupleto a slot 214. The slot 214 provides power and data to the processormodules. A chassis 218 may be provided, which contains a plurality ofslots 214. A passive backplane 220, which provides the data to the slotsand via the slots to the processor modules 208, 210, 212, is containedwithin the chassis 218. Within the chassis 218, a number of powersupplies 220 and fans 222 are included to provide power and aircirculation to the components of the chassis 218 as well as to theprocessor modules 208, 210, 212, which are physically coupled to thechassis 218. In embodiments, the flow processing facility 102architecture may support any number of processor modules 208, 210, 212in any permutation, limited only by the number of slots 214 in aparticular chassis 218. In applications, an administrator may physicallyadd or remove processor modules 208, 210, 212 from the chassis 218 byinserting or removing the processor modules 208, 210, 212 from theirrespective slots 214.

The application processor module 212 includes a host application ornetwork service that processes a data flow. The application processormodule 212 comprises one or more resident microprocessors eitherexecuting the host application or providing the network service.Applications and network services are distributed to and throughout theresident microprocessors. This distribution can include: the replicationof applications and network services; the configuration of them into afailover arrangement; and so forth. The application processor module 212is described in greater detail hereinafter with reference to FIG. 5.

Applications provided by the application processor module 212 may besoftware applications. These applications may be updated or maintainedfrom time to time (such as in response to a published bug fix) orperiodically (such as the daily retrieval of an application-specific logfile). Applications and the application processor modules 212 in whichthey reside can be grouped and can be managed as a group. This providesa level of convenience for an administrator of the flow processingfacility 102, who may want to update, activate, or deactivate groups ofapplications or application processor modules 212 without having torefer to each of the individual elements in the group.

One class of applications provided by the application processor module212 may encompass a content scanning function (which may encompasscontent inspection) for providing an anti-virus feature; an anti-spamfeature; an anti-spyware feature; a pop-up blocker; protection againstmalicious code; an anti-worm feature; an anti-phishing feature; or aprotection against an exploit. The anti-spam feature may be associatedwith a real-time black list; a DNS lookup; a header verification; akeyword filter; a spoof detector; an adaptive filter; and so forth. Theanti-spyware filter may be associated with scanning a download;monitoring for output communications from a spyware program; monitoringor regulating the use of cookies in applications; and so forth. Themalicious code protection may scan applications in-transit through theflow processing facility 102 for any kind of malicious code such as andwithout limitation a wabbit. The exploit protection may be directed atdetecting vulnerabilities in or exploits for ActiveX, Java, Flash,Javascript, Greasemonkey, JPG files, BMP files, Microsoft Office macros,and so forth. Content scanning may be applied to any data flow, forexample and without limitation data flows associated with an SMTPsession, a POP3 session, an HTTP session, or an FTP session.

A template can store a set of pre-configured parameters. Theseparameters may relate to applications or other elements of the flowprocessing facility 102, allowing the facility 102 and/or its elementsto be rapidly configured according to the parameters. An existingconfiguration of the flow processing facility 102 and/or its elementsmay be expressed at the template. Thus, the template may be used in abackup operation and a restore operation, both of which relate to one ormore configuration parameters of one or more elements of the flowprocessing facility 102.

The control processor module 208 coordinates the elements of the flowprocessing facility 102. These elements include the network processormodules 210, the application processor modules 212, and so on. Thecontrol processor module 208 enables management access to the flowprocessing facility 102 and its elements. This management access caninclude access to local facilities (memory, hard drives, network ports,network services and software applications, and so on) that residewithin the elements. The management server 228 receives or producesaggregate health and status information associated with the flowprocessing facility 102. Any function or feature of the flow processingfacility 102 that is subject to control by an administrator or aboutwhich information is provided to an administrator can be providedthrough a physical data port of the control processor module 208. Thisdata port can be operatively coupled to the management server 228.Through this coupling, information may be both received from themanagement server 228 and provided to the management server 228. Thisinformation may originate from the control processor module 208 or fromthe management server 228 and may be directed at controlling ormonitoring the flow processing facility 102.

In embodiments, the elements of the flow processing facility 102 areimplemented as processor modules 208, 210, 212 or “blades” which pluginto a chassis 218 that is implemented according to the networkarchitecture. In embodiments, the management server 228 is implementedin a host machine that does not plug into this chassis 218. Inembodiments, the flow processing facility 102 is implemented as arack-module with failover (such as and without limitation VRRP failover)or as a blade-chassis 218 module. The implementation of the flowprocessing facility 102 may include fully redundant elements andfeatures that support complete redundancy. These elements and featuresmay include the fans 222; the power supplies 220; the passive backplane224; data-switch fabrics; control-switch fabrics; control processormodule 208 with RAID-1 mirrored hard drives; active/active failoverconfiguration between two switches; logical interface redundancy (suchas and without limitation as may be provided by VRRP); applications(such as in a load-balancing and/or failover configuration); stateful,dynamic re-routing of data packets and flows; dynamic standby modulesfor M:N sparing; full hot-swap and zero-configuration replacement forfailed modules; a dedicated, high-availability link between elements;and so forth. The flow processing facility 102 may support a single-boxhigh availability mode (SBHA) or a multi-box high-availability mode(MBHA). In the preferred embodiment, the flow processing facility 102 isimplemented as a chassis-based system with no need for externalswitches, load balancers, taps, or port mirrors. The flow processingfacility 102 may support intelligent load balancing from the networkprocessor modules 210 to the application processor modules 212 basedupon actual usage metrics of the application processor modules 212. Theflow processing facility 102 supports serialization of the applicationsand network services. In other words, the flow processing facility 102can route a data flow between a series of applications and networkservices that are provided by the application processor module 212. Inone example, a data flow may be routed to a firewall application, thento an anti-virus application, then to a URL filter, then back to thefirewall. The flow processing facility 102 supports parallelization ofthe applications and the network services. In other words, the flowprocessing facility 102 can duplicate a data flow and simultaneouslyroute the duplicates to two different applications or network serviceswhich are provided by the application processor module 212. In oneexample, one of the duplicates is routed to an intrusion detectionapplication while another duplicate is routed to a URL filter. Manyother such examples will be apparent, will be discussed herein, or willbe discussed in the documents referenced herein, and all such exampleapplications of the flow processing facility 102 are encompassed by thepresent disclosure.

Referring now to FIG. 3, a detailed view of the network processor module210 is shown. The network processor module 210 may include a physicalnetwork interface 302; a switching fabric 304; a data flow engine 308;and a data flow processor 310 that includes content search logic 312;self-organizing map logic 314; and self-organizing map memory 318. Thenetwork processor module 210 communicates with the public network 202 aswell as the private network 204 via the physical network interface 302.The physical network interface 302 may encompass a physical networkport, plug, or socket. Switching fabric 304 provides a mechanism andlogic for communicating information between the network processor module210 and other modules 208, 210, 212 via the backplane 224.

Referring now to FIG. 3A, a detailed view of an alternate embodiment ofthe network processor module 210 is shown. As compared with the networkprocessor module 210 of FIG. 3, that of FIG. 3A is identical except thatit does not comprise the machine learning logic 314 and the machinelearning acceleration hardware 318. Generally, any and all descriptionsthat reference FIG. 3 or elements thereof do equally and simultaneouslyrefer to FIG. 3A, except where references to the machine learning logic314 or the machine learning acceleration hardware 318 necessarilyconstrain the description to reference FIG. 3 only (since FIG. 3A doesnot comprise these elements).

Referring now to both FIG. 3 and FIG. 3A, except in such cases as justdescribed, the network processor module 210 provides a physical and/orlogical interface to a communications system, such as an IP-based datanetwork, which may encompass the public network 202 and/or privatenetwork 204. This module 210 may contain one or more physical networkports or interfaces 302, which may accept physical connections to thecommunication system. It may be appreciated that any number of physicalconnections may be provided by the flow processing facility 102 throughthe addition of an adequate number of network processor modules 210 tothe chassis 218. Network processor modules 210 can contain a homogenousor heterogeneous collection of physical network interfaces 302. Thephysical interfaces 302 may be auto-sensing (such as and withoutlimitation a 10/100 auto-sensing Ethernet port), or, the physicalinterfaces 302 may have a fixed or manually configured setting such asand without limitation a dedicated uplink port or a port that isconfigured via a physical switch to perform as an uplink or downlinkport.

The network processor module 210 can receive and classify data flows.This classification can be related to any feature, aspect, or nature ofthe data flow or to any information that is associated with the dataflow. Some examples of these include source address, destinationaddress, time of day, day of week, user-agent token, the contents of apacket payload, and so forth. In any case, the classification may beused to drive a decision process which directs the data flow to anapplication processor module 212 via the passive backplane 224. As dataflows are routed between systems and elements according to the presentinvention, the data flows may be transmitted in a compressed format.These compressed flows may travel between blades, between chassis 218,between devices, and so forth.

It will be seen in the following figures and descriptions that the dataflow may return in an augmented, reduced, or otherwise altered statefrom the application processor module 212 back to the network processormodule 210. The network processor module 210 may further classify thedata flow; transmit the data flow to another application processormodule 212; transmit the data flow out to the public network 202 or theprivate network 204; or otherwise process, direct, redirect, return, ordiscard the data flow.

While the flow of data through the network processor module 210 isdescribed in great detail hereinafter with reference to FIG. 4, it isworth noting here that generally and without limitation a data flowarrives at the network processor module 210 via the physical networkinterface 302 or via the switching fabric 304 of the flow processingfacility 102. The data flow is then received by the data flow engine 308and then processed by the data flow processor 310. Depending upon theoutcome of this processing, the data flow engine 308 may direct the dataflow at one or more modules 208, 210, 212. When this occurs, theswitching fabric 304 receives the data flow and transmits the data flowvia the backplane 224 to the designated module(s) 208, 210, 212. Thus,one function of the network processor module 210 is to receive anddirect data flows.

A data flow may be directed according to one of its features, which aredescribed hereinafter with reference to FIG. 4. The data flow may bedirected at an external network device that is identified by a networkaddress such as an IP address, MAC address, URI, or any other networkidentifier. In this case, the data flow may be transmitted via thephysical network interface 302 to the external device. Alternatively,the data flow may be transmitted via the switching fabric 304 to anothernetwork processor module 210 that transmits the data flow via itsphysical network interface to the external device. Other suchconfigurations are possible and encompassed by the present disclosure.

The machine learning logic 314 classifies a data flow or portionthereof. In the preferred embodiment, the classification is binary, withsome data flows being classified as “normal” and others being classifiedas “anomalous.” Also in the preferred embodiment, the machine learninglogic 314 includes a self-organizing map or Kohonen map. Throughout thisdisclosure, the machine learning logic 314 may be described in thecontext of its preferred embodiment. However, any system or method forthe classification of data, whether or not drawn from the field ofmachine learning and whether or not associated with a binaryclassification scheme, may be utilized within the scope of the presentinvention as the machine learning logic 314. Therefore, all such systemsand methods are encompassed by this disclosure. Continuing now with thediscussion of the preferred embodiment, the classification of data isachieved by comparing a feature vector of the data flow with each of aplurality of artificial neurons that populate an array. Each of theartificial neurons is characterized by a weight vector. While thefeature vector and the weight vectors of the artificial neurons mayinclude an arbitrarily high number of dimensions, the array ofartificial neurons is typically two or three dimensional. In thepreferred embodiment, the array of artificial neurons is atwo-dimensional, 10-by-10 array. After an unsupervised training processin which weight vectors of the artificial neurons are adjusted, amapping process compares an input vector to the weight vectors. Theartificial neuron, characterized by the weight factor positioned at thesmallest Euclidean distance from the feature vector, is declared thewinning neuron, and the feature vector is this mapped to that neuron.Mapping may include incrementing or implementing a counter associatedwith the neuron, updating a running average associated with the neuron,and so forth. Over time, this mapping of feature vectors creates adistribution or “output map.” An anomalous data flow will produce anatypical output map by causing at least one of the values in the outputmap to become unusually large or unusually small in relation to theother values. Such anomalous data flows are flagged for additionalinspection.

During the training process, the artificial neurons are adjusted withrespect to or in response to training data. The training data maycomprise a set of feature vectors which are typically generated orextracted from one or more representative data flows. These data flowsmay be simulated or actual, and may be recently, currently, orpreviously generated. The type of features that comprise the featurevectors may depend upon a subject of the training process.

In embodiments, the subject of the training process may be associatedwith a networking behavior of a data flow and/or a content behavior of adata flow. In the case where the subject is associated with thenetworking behavior, the features may be related to one or more packetheaders and/or payloads that are associated with the data flow. In anexample and without limitation, the networking behavior may beassociated with a connection time, an inter-connection time, a requesttime, a response time, a count of a number of bytes in a connection, anyand all other features of the packet headers and/or payloads, and soforth. In the case where the subject is associated with the contentbehavior, the features may be related to one or more payloads that areassociated with the data flow. Here, the features may be extracted byusing sequential one- or two-byte chunks (referred to herein as a 1 Gramor 2 Gram) of the payloads. As each chunk is extracted, it is normalizedand then sorted, resulting in a profile. The profile may be divided intodiscrete and/or finite divisions. Each of these divisions may comprisesome or all of the occurrences of a 1 Gram or 2 Gram. In embodiments,the subject may encompass a count of the occurrences.

The machine learning logic 314 may normalize or convert the data flowinto a feature vector, which is the input vector to a SOM. The SOM maybe selected from a plurality of SOMs. This selection may be influencedby the inspection of the packet headers, payloads, protocol, behavior,and so on. In an example and without limitation, the SOM that isselected might correspond to the application associated with the flow.

Normalization of the data flow 444 may be with respect to any and allfeatures of the of the data flow 444. These features may, withoutlimitation, be associated with and/or comprise headers, payloads,protocols, behaviors, and so on. In an example and without limitation, anormalization of a data flow 444 may encompass a time at which a packetof the flow arrived (perhaps measured in milliseconds) and a size of thepacket (perhaps measured in millions). In embodiments, a normalizationof a data flow 444 may be expressed in terms of standard deviations ofmeasurements of features of the flow. More generally, in embodiments,the normalization may be expressed in terms of a statistical measure oras a concrete and tangible result of a mathematic calculation.

In embodiments, the mapping process is applied to feature vectors whichare generated from actual data flows that are specifically associatedwith network communications. In embodiments, the flows and/or theircontents are classified and a self-organizing map corresponding to thatclassification is used in the mapping process. In an example, oneself-organizing map may be trained with feature vectors from HTTPsessions while another is trained with feature vectors from SMTPsessions. When an incoming flow is recognized as being an HTTP session,feature vectors associated with that flow are mapped to the HTTP-trainedself-organizing map (SOM), and not the SMTP-trained SOM, and the sameprocess applies in reverse. Such recognition may be achieved byinspecting IP packet headers, IP packet payloads, destination portaddresses, URLs and so forth.

The mapping process involves computing the Euclidean distance between aninput vector and the weight vector of an artificial neuron. To expeditethis calculation, distance-computing circuitry may be provided. Thiscircuitry comprises distance-computing logic, contains memory forstoring a plurality of weight vectors, and encompasses a logic thatenables the memory to be indirectly addressed. In the preferredembodiment, the machine learning acceleration hardware 318 provides thiscircuitry. In other embodiments, the machine learning accelerationhardware 318 may be appropriately implemented to accelerate the machinelearning logic 314. The machine learning acceleration hardware 318 maycomprise a cache, an ASIC, an FPGA, a DSP, a quantum computing device,or any other hardware that accelerates or serves as a co-processor tothe machine learning logic 314.

As a flow is being mapped, its feature vectors may also be fed toanother SOM to serve as training data. The SOM that receives thetraining data is in the training process and may eventually replace theSOM with the corresponding SOM that is in the mapping process. Thisarrangement is advantageous considering that network data flows, due tomany factors such as network congestion, application usage patterns,user access patterns and so forth, are dynamic. Thus, the SOM that is inthe mapping process most likely trained on data that may now be outdatedand, therefore, may or may not reflect optimal and contemporary dataflows. Before that SOM becomes obsolete, a newly-trained SOM may replaceit. In this way, the system maintains a relatively current view of whatis “normal” and can continuously monitor data flows for anomalies.

While the training process may be deterministic, the SOMs that are fedinto the training process may initially contain randomized weights. Thisoccurrence may help ensure that the SOMs aren't biased before trainingbegins. One consequence of this randomization, however, is that theoutput maps of any two SOMs are likely to be quite different, even whenconsidering or assuming the possibility of identical training data andidentical input vectors. In the present invention, this may beundesirable because it might introduce a discontinuity when one SOM isreplaced with a newly-replaced SOM. In particular and as will beappreciated, a detection threshold or set of detection thresholds thatmay be applicable to the output map of the first SOM may not beapplicable to the output map of the second SOM. To avoid this, anadditional SOM may be added to the training process, whereby, beingalready biased by the SOM it is about to replace, its output map may besimilar to the output map of the SOM it is to replace.

The system can generate, in real time, an output map in response to adata flow 444. The detection process that is applied to the output isalso conducted in real time. When a data flow 444 is flagged asanomalous, it may be processed off-line and/or out of band, where a morein-depth analysis is performed.

The output maps may periodically be read and reset. When reading theoutput map, a test may determine whether the output map contains anindication of an anomaly. Alternatively, the values in the output may becontinuously normalized to represent running averages of the number offeature vectors that are mapped to each of the artificial neurons. Inany case, the indication of an anomaly will appear as an unusual,relatively high, or relatively low value in the output map. A detectionthreshold may previously be selected for each of values in the outputmap, wherein the threshold is statistically calculated to yield amaximal detection rate given a maximum false-positive rate. This ratemay vary from application to application. When the values in the outputmap exceed this threshold, the flow is flagged for additionalinspection.

As a data flow 444 is received at the network processor module 210, itspacket headers and/or payloads may be inspected. This inspection (whichwhen specifically directed at the payload may comprise contentinspection) may be performed by the content search logic 312 and mayencompass the inspection of source IP address, destination IP address,source port, destination port, application associated with the flow,user associated with the flow, content of the payload, and so forth. Inembodiments, the communication flow may be divided into chunks, whichmay be the packets.

In embodiments, a SOM may generate information (such as and withoutlimitation a signature) that is associated with a data flow 444. Thecompiler that may be provided in association with and/or as part of theflow processing facility 102 may process this information as input (asdescribed herein with reference to FIG. 3 and elsewhere). The output ofthe compiler (or the signature itself, if the compiler is not presentand/or not used) may be provided to the content search logic 312, whichmay then provide a content search functionality that is influenced bythe information or signature that may have been generated by the SOM.

The content search logic 312 may include an implementation of theAho-Corasick algorithm, an optimization or modification thereof, or anyother algorithm or heuristic for performing pattern matching, such asand without limitation regular expression matching, on a data flow. Thecontent search logic 312 may locate all instances of strings in the dataflow that match strings in a dictionary. The Aho-Corasick algorithm mayutilize a rooted tree structure (or, “pattern tree”) to represent a setof patterns. Each link (or, “transition”) between nodes may denote acharacter or token selected from an alphabet of the same. Each node inthe tree may represent a match of a prefix of one or more strings in thedictionary.

A pattern search process may start at the root node of the treestructure and with an input string. The input string may be a data flow444; or a segment, portion, or subset thereof. The process may traversethe tree by selecting, one by one, transitions that match successivetokens from an input string. The tree is traversed until the inputstring terminates; a leaf node is reached; or there are no transitionsout of a node that match the next token from the input string.

If there are no transitions out of the node and the node is notdesignated as a terminal node, then the input string may have failed tomatch a string in the dictionary. When such a failure occurs, apre-computed failure transition may be used to determine the next node.The failure transition may link to a node that corresponds to thelongest prefix of a string in the dictionary that matches the mostrecent tokens of the input string. This transition can be pre-computedfor each node because it may be solely dependent on data that is known apriori (i.e., the pattern of input tokens that reach the node where thefailure occurs and the prefixes of the strings in the dictionary). Oncethe failure transition is followed, the token that failed to match atransition may be applied again, this time to the node at thedestination of the failure transition. The pattern search process maycontinue in this manner until all of the characters of the input stringhave been applied.

The search algorithm may be further optimized by generating a failuretransition table. The failure transition table may also be pre-computedusing the pattern set and the matched prefix. The failure transitiontable may be calculated by finding the longest prefix of the pattern setfor all suffixes of the current node. This calculation may produce alist of failure transitions which may be judged to compose the failuretransition node(s). The links of all the failure transition nodes may bemerged to form a table of links where each link may be associated with apossible search character. When merging the links, the link in the nodewith the longest prefix may be given precedence. The resultant failuretransition table may then utilize a character as input to generate thenext node. Such a table (and other such structures that may similarly beconstructed) may provide one set of pre-computed failure transitions forall applicable characters. The failure transition table may be mergedsuch that the links in the node that may have existing links may begiven precedence. A pattern search procedure may then start and proceedin the same fashion as the original method. Since the failuretransitions and their ultimate destinations may now be built in to thenodes links, the fail character may not need to be applied again. Thefail character may be matched only once to proceed to the next node. Ifthe character does not correspond to any links in the node (includingthe failure transition links), the current string segment may not matchany patterns in the pattern set and the search resumes with the nextcharacter at the root node. The resultant search performance of theoptimized search may now be seen as linear to the size of the text.

The pattern tree can be viewed as the next state logic for a statemachine. In such a perspective, each node may be seen to represent astate in the state machine. The links, then, may compose transitionsfrom one state to another. The state machine may receive characters asinput and may uses this input (and/or may use other factors) tocalculate the next state.

If the input string terminates and the current node is specified as aterminal node, then the input string may match a string in thedictionary. Otherwise, the string may not match.

If the current node is a leaf node, then the only transition may be backto the root node of the tree. In the preferred embodiment, an optimizedrepresentation of the pattern tree may use a default value for the rootnode identifier. This may reduce the space required to store arepresentation of the tree.

In an example and without limitation, FIG. 9 depicts a tree built forthe pattern set AABA, ABEBE, ABF, BEBC, BEBB and BDD. Node 1 is the rootnode. Each node in the tree represents a prefix of one or more patternsin the pattern set, the bolded nodes representing complete patterns.Although all terminal nodes are leaf nodes in the example, this is not anecessary characteristic. A searched-for pattern that is anothersearched-for pattern's prefix will result in a terminal node that is nota leaf node. A straight line represents a state transition based on thesuccessful match of the next character in the search text. The curvedlines are state transitions taken when the match of the next characteris not successful. When there are no curved lines from a state, a failedmatch will cause a state transition to the root.

The example shown in FIG. 9 searches for a single match. If all matchesare desired, the pattern search can continue at the terminal node byadding the failure transitions. In this case node 16 will have a failuretransition to node 3, node 13 will have a failure transition to node 2,and node 17 will have a failure transition to node 6. All the otherterminal nodes have failure transitions to the root node.

To illustrate how the algorithm works, suppose that the input string isABEBC and that it is to be searched simultaneously for all of the searchpatterns, i.e., AABA, ABEBE, ABF, BEBC, BEBB and BDD. The state machinestarts at node 1 and, since the first input-string character is A,follows the A edge from node 1, i.e., the edge that leads that node tonode 2. The next input-string character, B, matches an edge from node 2,so the state machine follows that edge to node 5. The next character, E,causes a transition to node 9. Node 14 is entered on the next characterB. The next character C does not match any edge from node 14, so thefail transition is taken to node 11. There the machine again tries tomatch character C. This time a match is found and state 15 is entered.State 15 is a terminal node and indicates that the input string includesone of the search patterns namely, BEBC.

Note that in the example the last character in the search text wascompared twice. A common optimization of the Aho-Corasick algorithm isbased on the fact that the possible character matches for a failed nodemay be known in advance. Thus, when the failure-transition table isincorporated into the links at each node, the tree illustrated in FIG.10 may result. The tree in FIG. 10 includes the fail transitions for theterminal nodes. By employing this optimization, an attempt to match thesearch character need only be made once. (It will be appreciated thatthe tree may be omitting a number of possible fail transitions and thatthese omissions may or may not be for the purpose of simplifying thedepiction.)

A further detailed description of one implementation of the techniquesassociated with content search logic 312 and implementations of theAho-Corasick algorithm, (and an optimizations and/or modificationsthereof) may be described hereinafter or elsewhere.

In embodiments of the content search logic 312, a hardware-based stringsearch supports position constraints. In embodiments and withoutlimitation, search parameters or signatures for this search may beexpressed in the SNORT language. In any case, search parameters orsignatures may specify position dependent patterns; absolute positionpatterns; relative position patterns; and negative and positivepatterns. The position dependent patterns relate to a specific positionin a packet. The absolute position patterns relate to a position fromthe beginning of a packet. The relative position patterns relate to aposition relative to a previous pattern match.

A compiler may be provided in association with and/or as part of theflow processing facility 102. The compiler may process input that isassociated with search parameters, regular expressions, signatures, orany and all other specifications of content search, pattern matching,position constraints in string search, and so forth. The compiler maycompile this input into an output that is directed at and/or suitablefor programming and/or instructing any and all of the computationalhardware of the flow processing facility 102. In embodiments and withoutlimitation, such hardware may comprise one or more of a digital signalprocessor; an FPGA; a particular brand, model, or series of centralprocessing unit; an ASIC; and so forth. Without limitation, the contentsearch logic 312 may encompass this hardware. It will be appreciatedthat the compiler may enable the processing of any and all searchparameters or signatures such that the parameters or signatures are sotransformed.

Embodiments of the content search logic 312 may encompass hardware-basedregular expression matching logic. This hardware matches input stringsto regular expressions. The regular expressions may include characters,quantifiers, character classes, meta-characters, and so forth. Thematching may be greedy or non-greedy and may include look-head andlook-behind functionality. In alternate embodiments, the hardware alsosupports back-references. This hardware may include a hardwareimplementation of the Aho-Corasick algorithm, an optimization ormodification thereof, or any other algorithm or heuristic for performingregular expression matching on a data flow.

Embodiments of the content search logic 312 may encompass hardware-basedregular expression logic while performing a search for positiondependent substrings. To this end, a regular expression may first bepartitioned into a set of position dependent substrings. A pattern treemay then be constructed which represents and enacts the search forsubstrings. When a substring is found, the relative positions of thesubstrings may be examined and, depending upon the result of theexamination, a positive or negative match may be effectively determined.The logic may include the capability of detecting character classes(such as /[abc]/) and wildcards (such as * and .) which may be includedin the regular expression. The logic may be capable of matching thebeginning as well as the end of a string. Additionally or alternatively,the hardware-based regular expression logic can match alternation (suchas /cat|dog/—“match ‘cat’ or ‘dog’”). In an embodiment, all possiblematches resulting from an alternation may be built into the patterntree. In another embodiment, the character class detector may beemployed to match alternation. Alternately or additionally, thehardware-based regular expression logic may be able to match repetitivepatterns (e.g. patterns repeated using quantifiers such as /a{3}/—“match‘aaa’”). In an embodiment, the repetition may be unwound and theresulting patterns may be inserted into the pattern tree.

Example implementations of the foregoing may be provided hereinafter orelsewhere.

Referring now to FIG. 4, a process and logical flow of the flowprocessing facility 102 involves the data flow engine 308. Generally,the process and logical flow are directed at receiving, processing, and,when appropriate, transmitting a data flow 444. In the preferredembodiment, the data flow 444 is composed of an IP-packet sequence, suchas may be associated with a connection-oriented protocol (e.g., TCP/IP)or a connectionless protocol (e.g., UDP/IP). Each packet and, byextension, the data flow 444, may be composed of packet headers andpacket payloads. Both headers and payloads may comprise digitallyencoded information. The headers may conform to a network protocol'sspecification or, in some malicious or erroneous cases, may defy thenetwork protocol's specification. The payloads may embody informationdirected at an application and/or encapsulated packets (or fragmentsthereof). It will be appreciated that features of the data flow 444 may,without limitation, comprise a field, flag, code, or other informationin a header; a particular value of a field, flag, code, or otherinformation in a header; a sequence of those values across a pluralityof headers; a difference or other relation between two or more headers;a timing associated with one or more headers (for example and withoutlimitation, an arrival time, a inter-arrival time, a response time, alag time, and so forth); a count or size associated with one or moreheaders (for example and without limitation, a size of the header asmeasured in bytes, a size of a payload as indicated in the header, asequence number or count of the packets in the data flow 444 asindicated in the header, a count of a plurality of headers; and soforth); a value in a payload; a sequence of values in a payload; asequence of values across a plurality of payloads; a difference or otherrelation between two or more payloads that are associated with the dataflow 444; a timing associated with a payload (such as and withoutlimitation, an arrival time, an inter-arrival time, a response time, alag time, and so forth); a count or size associated with one or morepayloads (for example and without limitation, a size of the payload asmeasured in bytes; a cumulative size of the payloads; a projected orexpected size of the payload; a projected or expected cumulative size ofthe payloads; the number of payloads associated with the data flow; andso forth); and so on.

The data flow 444 may be received at the physical network interface 302and then provided to the data flow engine 308. There, the data flow 444,which may be embodied as one or more network data packets 402, may beduplicated. One of the duplicate data flows 444 may proceed to a cellgenerator 404, while the other may be routed to the data flow processor310.

The cell generator 404 may convert the packet 402 into a data cell 408,which may simply be an alternate representation of the packet 402. Thisdata cell 408 may be optimized for transmission via the backplane 224and the switching fabric 304. The data cell 408 may also be optimizedfor communication between the network processor module 210 and theapplication processor module 212.

From the cell generator 404, the data cell 408 is transmitted to a cellrouter 410. The cell router 410 may consider the data 408 in light of anapplication identifier 412 and security policy 414. Based upon thatconsideration, the cell router 310 may direct the data cell to theapplication processor module 212; to a packet generator 418; or to adone logical block 420. The application processor module 212 can receivethe data cell 408 from the cell router 410, process the data cell 408;and return the data cell to the cell router 410. This processing of thedata cell 408 by the application processor module 212 is described indetail hereinafter with reference to FIG. 5. The packet generator 418can receive the data cell 408 and transform it into a packet 402,wherein both the data cell 408 and the packet 402 are elements of a dataflow. These packets 402 are transmitted as a data flow to the physicalnetwork interface 302 from which they are transmitted out of the flowprocessing facility 102. The done logical block 420 is provided toillustrate that some data cells may be discarded by the cell router 410.The reasons for discarding data cells are numerous, but some examplesinclude reducing network congestion associated with the data cell;reducing resource utilization associated with the data cell; eliminatinga data cell that is associated with a prohibited application, source,destination, or some such; and so forth.

The application identifier 412 may be associated with an applicationgroup 422, which may be associated with a normalized data type 424,which may be associated with normalized data 428. The applicationidentifier 412 relates to an application that is or could be operatingin an application processor module 212. One or more applicationidentifiers 412 may be associated with an application group 422, whichmay simply be a set of application identifiers 412 that are providedtogether as group. The normalized data type 424 may simply indicate thetype of the normalized data 428. The normalized data 428 may encompass arepresentation of the data flow 444.

In the interest of providing at least a semblance of visual clarity,each of the elements of FIG. 4 is depicted as a singular block.Particularly in this figure and generally in all figures, it may beappreciated that any of the elements of a figure may encompass aplurality of such elements, even in cases where the depiction may seemto suggest otherwise. Thus, in embodiments, there may be a plurality ofnetwork data packets 402; cell generators 404; data cells 408; cellrouters 410; application identifiers 412; security policies 414; packetgenerators 418; done logical blocks 420; application groups 422;normalized data types 424; normalized data 428; identifiers 430;customer identifiers 432; service identifiers 434; service levelidentifiers 438; other identifiers 440; alerts 442; data flows 444;fingerprints 448; action rules 450; header rules 452; content rules 454;physical network interfaces 302; application processor module 212s 212;and so forth.

The security policy 414 may be associated with an identifier 430, whichmay be associated with the normalized data 428. The identifier 430 mayinclude one or more of the following: a customer identifier 432; aservice identifier 434; a service level identifier 438; or anotheridentifier 440. The security policy 414 may specify any number oflimitations or conditions that may be applied to the data flow 444 orits corresponding data cells 408. Alternatively or additionally, thesecurity policy 414 that may be associated with an application thatresides within the application processor module 212. In someembodiments, the security policy 414 specifies that certain data cells408 may be processed by the application processor module 212 whileothers may not. For those cells that may not be processed, the securitypolicy 414 may specify whether the data cells may be passed through tothe packet generator 418 and out of the flow processing facility 102, orwhether the data cells may be routed to the done logical block 420,where they are discarded (or, perhaps, logged—but in either case notallowed to leave the flow processing facility 102).

The identifier 430 of the normalized data 428 may encompass metadatarelated to the normalized data 428. In one example, which is presentedhere for the purpose of illustration and not limitation, the normalizeddata 428 is related to a customer that is assigned a customer identifier432. In the networked computing environment 100, the customer may be anin-house customer, who may be associated with the departmental computingfacilities 110. Alternatively, the customer may be an outside customer,whose computing facilities are operatively coupled to the internetwork104. In this case, the data flow 444 may originate from an applicationor computer system that is associated with or operated by the customer.Depending upon a business relationship between the operator of the flowprocessing facility 102 and the customer, a particular security policy414 may be associated with the customer. In one example, a customer isdenied access to the departmental computing facilities 110 but isgranted access to the server computing facilities 108. When normalizeddata 428 that is associated with the customer is present, the chain ofassociations between that data 428, the customer identifier 432, and thesecurity policy 414 will be invoked. The cell router 410 may act inaccordance with the invoked security policy 414, causing all data cells408 that are of a data flow 444 of the customer and that are addressedto the departmental computing facilities 110 to be routed to the donelogical block 420. Many other such examples are described herein andwill be appreciated from the present disclosure, and all such examplesare encompassed by the present invention.

In some cases, the normalized data 428 is related to a service that isassociated with a service identifier 434. In the networked computingenvironment 100, the service may be provided by the flow processingfacility 102. Alternatively or additionally, the service may be providedby a server computing facility 108, the departmental computingfacilities 110, or any other computing facilities that are operativelycoupled to the flow processing facility 102 or the internetwork 104. Inone application and without limitation, the service is a peer-to-peernetworking technology that is provided by two computing facilities 108that are operatively coupled via the flow processing facility 102. Asecurity policy 414 that denies transmission of a data flow 444 may beassociated with a service identifier 434 that is associated with anormalized data 428 representation of a peer-to-peer data flow 444. Inthis way, when such normalized data 428 is present, the chain ofassociations between that data 428, the service identifier 434, and thesecurity policy 414 will be invoked. The cell router 410 may act inaccordance with the invoked security policy 414, causing all data cells408 that are of the peer-to-peer data flow 444 to be routed to the donelogical block 420.

In some cases, the normalized data 428 is related to a service levelthat is associated to a service level identifier 438. In the networkedcomputing environment 100, the service level may be associated with aservice that is provided by the flow processing facility 102.Alternatively or additionally, the service may be provided by a servercomputing facility 108, the departmental computing facilities 110, orany other computing facilities that are operatively coupled to the flowprocessing facility 102 or the internetwork 104.

When the service encompasses a peer-to-peer networking technology, itmay relate to two or more computing facilities 108 that are both engagedin a peer-to-peer application and operatively coupled via the flowprocessing facility 102. A security policy 414 that denies transmissionof a data flow 444 may be associated with a service identifier 434 whichis associated with a normalized data 428 representation of apeer-to-peer data flow 444. In this way, when such normalized data 428is present, the chain of associations between that data 428, the service434 identifier, and the security policy 414 will be invoked. The cellrouter 410 may act in accordance with the invoked security policy 414,causing all data cells 408 that are of the peer-to-peer data flow 444 tobe routed to the done logical block 420.

Generally, the normalized data 428 may be related to something that isassociated with an identifier 430. For the purposes of capturing thisnotion, the other identifier 440 is provided to emphasize that any andall identifiers 430 that will be appreciated or that are mentionedherein may be represented and utilized according to the presentinvention. Many other such examples are described herein and will beappreciated from the present disclosure, and all such examples areencompassed by the present invention.

In embodiments, a system according to the present invention includeshardware-based logic that matches action rules 450 to packets 402 and/ortheir corresponding data cells 408. The cell router 410 and/or the cellgenerator 404 may encompass this hardware-based logic. The logic mayaccept an action rule. The action rule 450 may include a header rule452, which describes an aspect of a header such as protocol type, sourceaddress, destination address, source port, destination port, TCPdirection, and so forth. The action rule 450 may additionally include acontent rule 454, which relates to a transport-level payload, such asthe payload of one or more TCP packets. The header rule 452 may bedesignated as focused and only one focused rule can match a givenpacket. The header rule 452 may be designated as promiscuous and anynumber of promiscuous rules can match a given packet or data cell 408. Acompact representation of the header rule 452 may be provided. Thisrepresentation may explicitly represent a focused header rule 452combined with a representation of one or more promiscuous header rules452. (Details on the methods that implement these compaction techniquesmay be found below in paragraph 200.) Regardless of its representationor designation, a header rule 452 may relate to an action of the cellrouter 410. In particular, the action may encompass both routing a datacell 408 to a particular application processor module 212 and addressingthe data cell 408 to a particular application within the applicationprocessor module 212.

In embodiments, the action rules 450 may specify an action that occurswhen the header rule and/or content rule match an aspect TCP packet 402or a sequence of TCP packets 402. The action can be to pass or drop thepackets 402. The action can be to receive, modify, and send the packets,resulting in a modification to the headers and/or payloads of thepackets 402. The action can be to receive, process, and send a responseto the packets 402, such as may occur in a proxy or cache that itselfrecognizes a request in the payloads of the packets 402. In this way, adata flow engine 308 may respond, just as a proxy or cache would, to arequest without passing the packets 402 or data cells 408 associatedwith the request to the destination specified in their headers.

In embodiments, an action rule 450 may specify an action that triggers atransaction. The transaction may encompass a financial transactionassociated with the provision of a service. In an example and withoutlimitation, an owner or operator of the flow processing facility 102 mayautomatically charge a fee every time the data flow engine 308 respondsto a request as a proxy or cache would. Alternatively or additionally,the transaction may encompass a database transaction. In an example andwithout limitation, a modification to a logging database may beconducted and committed in response to packet 402 or data cell 408 thatmatches the action rule. The logging database may contain a log ofalerts 442, packets 402, data cells 408, or information associated withany and all of the foregoing. The logging database may be providedand/or maintained by a management server 228; a flow processing facility102; a computing facility that is operatively coupled to a flowprocessing facility 102 via a physical network interface 302. Many othersuch examples involving a transaction will be appreciated and all suchexamples are within the scope of the present disclosure.

In embodiments, the action rule 450 may specify an action that triggersa translation of one protocol to another, where the protocol may be atthe application level, the transport level, the network level, the linklevel, or any other such level.

The present invention may include a subscriber profile. This profile mayrelate an application to a subscriber. In doing so, it may specifyaccess control rules, privileges, and preferences associated with thatrelation. Systems and methods of the present invention can store,distribute, modify, act in accordance with, and enforce aspects of thesubscriber profile. The action rule may specify an action that comportswith the subscriber profile. In an example and without limitation, theaction rule may specify that packets 402 or data cells 408 that areassociated with a subscriber get a higher priority than those that arenot associated with the subscriber. This higher priority may entitle thepackets 402 or data cells 408 to faster processing, higher bandwidth,lower latency, a preferred route, and so forth.

In embodiments, a system according to the present invention may includehardware-based logic that reassembles a data flow 444 from TCP packets402. This logic, which may be encompassed by the cell generator 404,includes a replay process, which repeats current data and appends apacket to a TCP flow 444. The replay process may be recursive; theappended packet 402 may become part of the current data, to which thereplay process can again be applied. The logic may also includepattern-matching circuitry (such as the regular expression logic, whichmay be an embodied as the content search logic 312) that triggers thereplay process on a partial rule match, exemplified when a pattern in anaction rule straddles a packet boundary. In this way, a data flow 444can be replayed any number of times, with the replays being presented topattern-matching circuitry associated with action rules 450. The dataflow 444 can be incrementally extended as the payloads of additional TCPpackets are appended to the data flow 444.

In some circumstances, the data flow engine 308 may issue an alert 442.The alert may be in the form of a data element, an electric signal, anaudible or visible annunciation, and the like. The issuance of the alertmay pertain to a condition of the data flow engine 308, such as andwithout limitation an internal error, a pending failure, a statusreport, and so forth. The alert may be provided to another element ofthe flow processing facility 102, to a human operator of the flowprocessing facility 102, or to any other facility capable of receivingthe alert. In some embodiments, the alert may be transmitted via awireless or wired communication link that may or may not be theinternetwork 104, the backplane 224, the switching fabric 304, and soon.

It will be appreciated that, throughout this disclosure and in any andall disclosures included herein by reference, that data flows 444 (andtheir constituent packets 402 or cells 408) may encompass informationthat is encoded by different layers of a network stack. In an exampleand without limitation, the networks stack may comprise the InternetProtocol (IP) stack.

Referring to FIG. 31, a data flow 444 may be composed of an IP-packetsequence that adheres to an Internet Protocol (IP) stack 3100. The IPstack 3100 will be familiar to those skilled in the art. Higher layerpackets may be encapsulated in the payload of lower layer packets suchthat network communication devices that operate at the lower layer maytransfer packets with arbitrarily complex payloads without regard forthe complexity or content of the payload.

The uppermost layer is the application layer 3110. This layer 3110 maybe used to define the particular data and/or data structures thatapplications may communicate. This data may be application specific andits design may be left to an application developer. In this way, anyapplication-to-application communication may be developed and/orspecified independently from the transport mechanism used to communicatethe data between applications.

Application-to-application communications (i.e. one or more applicationlayer 3110 packets) may be encapsulated in one or more transport layer3112 packets. The transport layer 3112 may provide communicationspecifications that relate to the transport of data betweenapplications. In embodiments, these specifications may be implementedacross a plurality of computing facilities, providing a standardabstraction (or set of such abstractions) on top of which theapplication layer 3110 may reside. These abstractions may providestandardized systems and methods of communication between applications.In an example and without limitation, UDP, a transport layer 3112protocol, provides the abstraction known as ports to facilitateapplication-to-application communication; TCP, a transport layer 3112protocol, provides ports and also provides reliable, in-order datadelivery. Many other transport protocols may operate at the transportlayer 3112.

A transport layer 3112 packet (header and payload) may be encapsulatedin the payload of a network layer 3114 packet. The network layer 3114may enable the transfer of data between host computers over a network,perhaps without regard to the particular applications that may becommunicating via the data. Network services associated with the networklayer 3114 may include routing network layer 3114 packets from a sourcehost to the destination host.

A network layer 3114 packet may be encapsulated in the payload of a datalink layer 3118 packet. The data link layer 3118 may be associated withthe transfer of data between physical nodes in a network. In an exampleand without limitation, the data link layer 3118 may be associated withEthernet, WiFi, Token ring, and so on. Thus, a network layer 3114 packetmay be formed in accordance with the requirements of a physical datalink. Those skilled in the art will appreciate that an alternateembodiment of the data link layer 3118 may consist of frames containingpayloads, wherein each frame may comprise a frame header, the payload,and a frame trailer. Thus, in the present disclosure, any reference to aheader in the data link layer 3118 may refer to a packet header or toboth a frame header and trailer, depending upon the embodiment of thelayer 3118.

Any and all of the systems and methods of the flow processing facility102 may be directed at content inspection. Many examples of contentinspection are described herein and will be appreciated. All suchexamples are within the scope of the present disclosure.

Referring now to FIG. 5, the application processor module 212 mayinclude the switching fabric 304 and a plurality of applicationprocessing units 502. Each of the application processing units 502 mayinclude an application accelerator 504, a central processing unit (CPU)508, a random access memory device (RAM) 510, and a plurality ofapplications 512. The applications 512 may include a unified threatmanagement (UTM) application, which in turn may encompass one or more ofa firewall application 514; an intrusion protection system (IPS)application 518; an anti-virus application 522; a URL filter application524; an anti-spam application 528; and another UTM application 530. Theapplication processing unit may be a logical or physical unit,encompassing one or more hardware devices or software applications. Theapplications 512 may also include another, non-UTM application 532. Manyaspects of the application 512, the application processing unit 502, andthe application processor module 212 may be described hereinabove withreference to other figures.

In the preferred embodiment, the application processing unit is acommercial-off-the-shelf (COTS) computer or emulates the same. Theapplications 512 may be software applications that are uploaded, stored,and/or built into the application processing unit 502. In the preferredembodiment, the applications 512 are best-of-breed software applicationsthat are not specifically designed for operation in a flow processingfacility 102. In particular, the applications are preferably, but notnecessarily implemented, for COTS computers. Since the applicationprocessing unit is a COTS computer or emulates a COTS computer, theapplications are capable of operating within the application processingunit 502 as though they were operating within a COTS computer.

The application accelerator 504 may be a specialized hardware device foraccelerating a computational feature of an application 512. In oneexample, the application accelerator 504 is a cryptographic accelerationengine for encrypting and decrypting data. The application 512 may bedesigned to utilize the application accelerator 504. Alternatively, theapplication processing unit 502 may automatically utilize theapplication accelerator 504. In an example, the application accelerator504 may comprise an FPGA and the application processing unit 502 mayprofile the execution of the application 512 in order to identify acritical section of the application 512 that is compute intensive.Providing an accelerated execution of the critical region, this criticalsection may be dynamically programmed into the FPGA. Many such examplesrelating to the application processor module 212 and its elements willbe appreciated from this disclosure and all such examples are objects ofthe present invention.

In embodiments, a UTM application may encompass a system or method thataccepts a data flow 444 and classifies it according to whether or not amore detailed inspection of the flow 444 is warranted. If the detailedinspection is warranted, the UTM application may communicate and/orrefer the data flow 444 to the application accelerator 504 for furtherprocessing. This further processing may include processing of headers,payloads, protocols, communication traffic patterns, behaviors, and soon. In any case, the application accelerator 504 may be directed atproviding real time processing of the flow 444. This further processingmay be directed at providing one or more aspects of unified threatmanagement, such as those described herein and elsewhere, and those thatwill be appreciated.

The CPU 508, in the preferred embodiment, is a COTS CPU such as an IntelXeon processor, a Sun Sparc processor, or any other processor. The RAM510 may be any embodiment of RAM, including SRAM, DRAM, Flash RAM, andso forth. Many of the applications 512, 514, 518, 520, 522, 524, 528,530, 532 are herein described in detail and/or will be appreciated fromthe present disclosure. All such applications are within the scope ofthe present invention.

Referring now to FIG. 6, an example sequence of events 600 shows how theflow processing facility 102 can adapt to changeable data flowconditions. The figure presents six snapshots of the flow processingfacility 102. Each snapshot includes two application processor modules212, a network processor module 210, and a control processor module 208.Arrows that are unassociated with those modules indicate the progressionof snapshots as the flow processing facility 102 adapts to changes inthe data flow 444 over time.

The first snapshot is the top, leftmost snapshot. Here, a data flow 444enters the network processor module 210, which routes the data flow toan application processor module 212. The application processor module212 returns the data flow back to the network processor module 210,which transmits the data flow 444 out of the flow processing facility102. It will be understood from the foregoing discussion with referenceto previous figures that the data flow 444 may be represented at timesas packets and at times as data cells. It will also be understood thatthe data flow 444 or elements thereof may be modified by an applicationresiding in the application processor module 212.

In the next snapshot, which is directly to the right of the firstsnapshot, the data flow 444 as it first arrives at the networkprocessing module 210 is of such a nature that processing it at oneapplication processor module 212 would exceed the capabilities of thatmodule. This nature may relate to network bandwidth, processor or CPUbandwidth, RAM-related requirements, and so forth. In any case, theapplication processor module 212 recognizes that it is incapable ofcompletely processing the data flow 444. While continuing to process thedata flow 444 to the greatest extent that it can, the applicationprocessor module 212 transmits an application-alert signal 602 to thecontrol processor module 208. This application-alert signal 602 servesto notify the control processor module 208 that an overload conditionexists at the application processor module 212. The alert signal 602 mayfurther indicate the nature of the overload or any other data ormetadata associated with the overload. The control processor module 208receives the alert signal and processes it.

In the next snapshot, which is directly to the right of the last one,the overload condition persists. The control processor module 208transmits three signals (S1, S2, S3), one directed at each of the twoapplication processor module 212s and one directed at the networkprocessor module 210. The signal S1 to the application processor module212 that is currently handling the data flow 444 may encompass anacknowledgement of receipt of the application-alert signal 602. Thesignal S2 to the network processor module 210 may encompass instructionsto begin dividing the data flow 444 into two data flows 444. The signalS2 may further encompass instructions to transmit one data flow 444 tothe presently active application processor module 212 while transmittingthe other data flow 444 to the presently inactive application processormodule 212, which is the topmost APM 212 in the present snapshot. Thesignal S3 may encompass instructions to the inactive applicationprocessor module 212 to configure itself to accept a data flow 444 andto process that data flow 444 with a particular application 512 or setof applications 512. These applications 512 may be the same application512 or applications 512 that are presently processing the data flow 444at the active application processor module 212. In other words, thepresently inactive application processor module 212 may be configured inresponse to the signal S3 to replicate the functionality of the activeapplication processor module 212.

In the next snapshot, which is directly below the last one, the dataflow 444 into the network processor module 210 is the same as in theprevious snapshot. However, now the network processor module 210 dividesthe incoming data stream into two smaller data flows 444. Both of theapplication processor modules 212 receive one of these data flows 444.These data flows 444 are of a nature that the receiving applicationprocessor modules 212 can process them without creating an overloadcondition. Having processed the data flows 444, the applicationprocessor modules 212 return the data flows 444 to the networkprocessing module 210, where they are reunited into a single data flow444 that is transmitted out of the flow processing facility 102.

In the next snapshot, which is directly to the left of the last one, thedata flow 444 arriving at the network processor module 210 is of areduced nature as compared with what it was in the previous snapshot. Asbefore, it is divided and each of the resultant data flows 444 aretransmitted, received, and processed as before. However, as depicted bydotted lines, the data flows 444 resulting from the division are of anature that a single application processor module 212 could process bothof them without creating an overload condition. In other words, at thispoint it is now unnecessary to divide the data flow 444 as it firstarrives at the network processor module 210. The network processormodule 210 recognizes this condition and transmits a network-alertsignal 604 to the control processor module 208. The control processormodule 208 receives this alert 604 and processes it.

In the next snapshot, directly to the left of the last one, the dataflow 444 and its division, transmission, reception, and processing areas they were in the previous snapshot. Here, the control processormodule 208 transmits a signal S4 to the network processor module 210.This transmission may be in response to the network-alert signal 604.The signal S4 may encompass an instruction to the network processormodule 210 to cease dividing the incoming data flow 444 and, instead, toresume the original mode of operation as depicted and described withreference to the first snapshot. The network processor module 210receives and processes this signal S4. The network processor module 210complies with this signal S4 and the flow processing facility 102assumes the configuration of the first snapshot.

Although the invention can be used in a wide variety of applications,the following examples describe its application intelecommunications-traffic monitoring and, without limiting otherapplications and embodiments, may illustrate some of its novel features.

FIG. 7 depicts an embodiment of an aspect of present invention. Amonitoring system 700 (which may be subsumed within content search logic312) receives an input data stream 702 and employs an apparatusreflected in the elements with FIG. 7. In some implementations, it maybe desirable to process input data stream 702 in small units, typicallybytes. Note also that in practice, some embodiments of the invention mayor may not segregate functions among discrete hardware and/or softwaremodules according to a precise fashion, such as that shown in FIG. 7. Itwill be appreciated that the layout of the drawing in FIG. 7 may serveto illustrate, without limitation, a method wherein functionality of thepresent invention may be embedded. In this sense, FIG. 7 and relateddrawings may provide a simplified view of the present invention forpedagogical purposes, in a manner that conveys novel teachings of thepresent invention.

Character issuer 704 accepts input stream 702 and may formulate arepresentation of a new character (which may be composed as a byteand/or other data unit) which may be presented together with anindication of that character's position input data stream 702. Node RAM706 (and/or some read/write capable device) may containnode-representing data structures whose contents may have beendetermined from some set of one or more of patterns to be matched.Matching engine 708 may be used to fetch node identifiers (and/or otherrelated information) from node RAM 706 by presenting addressesconstructed from input data stream 702 and a last-fetched nodeidentifier. Note, however, that, as detailed below, the last-fetchedidentifier may not have been fetched from node RAM 706. One possiblemethod for executing this construction mechanism may be described indetail hereinafter or elsewhere.

The data fetched by matching engine 708 may include (but may not belimited to) a node identifier, but may also include an indicator ofwhether that node represents the end of a complete match that may berequired by one or more predefined rules. When matching engine 708thereby detects a match, it may present an address to table RAM 710dictated by the match-indicating node's identity. That address may pointto one or more list(s) of rules that may be related to detection of thispattern and that may require enforcement when such a string has beenfound. From this information, matching engine 708 may produce output 712that may cause any appropriate action to be taken.

As was mentioned above, while FIG. 7 illustrates one possibleimplementation, there may be many, perhaps widely varied architectureswithin which the present invention's teachings can be implemented. In anexample, they can be implemented in any general-purpose digitalcomputer; in dedicated or application-specific hardware; in any and allcombinations of the foregoing; and so on. In any and/or all of thesevariations, the functions that may be associated with the content searchlogic 312 or any and all other elements of the present invention may bedistributed or arranged in any number of ways within an embodiment,wherein any particular arrangement may be suited for or directed atrequirements that are associated with a use and/or context of theembodiment.

FIG. 8 depicts in more detail the data flow associated with matchingengine 708. The individual character from the “packet data” may be usedconcurrently in addressing both other-node RAM 804 and root-RAM 806. Inthe illustrated embodiment, root-RAM 806 may contain only the root nodeof a pattern tree (i.e., a data structure that lists the root node'schild nodes), although other embodiments that employ such concurrentnode addressing may also include the root node's closest descendantnodes. The other-node RAM 804 includes all the other nodes. Since thesingle node that root-RAM 806 contains may ordinarily require less thana single kilobyte of storage, the integrated circuit within thisembodiment's matching engine 708 may include root-RAM 806 on board.

In embodiments, pattern trees (such as those of FIG. 9 and otherfigures) that may reach several thousand nodes (or more), and thestorage requirements of such trees or associated arrays may exceed, forexample and without limitation, half a megabyte of memory. Thus, someembodiments may provide other-node RAM 804 in a separate dedicatedread/write capable device of sufficient capacity and speed to supportstorage and/or processing of such pattern trees.

Since, in some implementations, other-node RAM 804 may contain manynodes, addressing requirements may require both a high-order,node-indicating portion and a lower-order, link-indicating portion(where the latter identifies an entry within the node). Each such entrymay both 1) identify a respective child node; and 2) indicate whetherthat node is a terminal node. The node-identifying portion of the RAMoutput that results from addressing such an entry may form thehigh-order bits of the address that will be next applied to theother-node RAM 804. The low-order bits of that address may then bederived from the next input character.

It may be desirable in some implementations to provide two separatematching engines 708 with respective read/write capable devices forstorage of node information. In such cases, both matching engines 708may receive the same data, but the tree structures in their read/writecapable devices would represent different patterns. One advantage ofsuch an extension may be that two pattern matchers doubles the amount ofpattern memory and may allow some patterns to be preprocessed. One ofthe pattern matching engines 708 can, for example and withoutlimitation, be dedicated to case-insensitive patterns by changing thecase of the string before the search operation. This may improve thetree's efficiency and may additionally reduce the amount of memory usedfor each pattern.

But note that there is no limit on the number of pattern matchingengines that may be integrated, and though this may entail additionalcomplexity, in some implementations, the optimizations that may resultjustify the cost. One implementation, for example, supports four patterntrees. In this case, one pattern tree is dedicated to patterns in theURL. A second pattern tree is used for decoded telnet (i.e. preprocessedtelnet data located in the decode buffer). The third pattern treecontains the rest of the search patterns. The fourth pattern tree isreserved for future optimization. In this particular implementation,each pattern tree has its own on-chip root node and its own initial rootnode, but this optimization is optional and represents one of manypossible optimizations that may be employed.

Embodiments of the present invention also deal with the conditionwherein the next input character results in a failure (that is, whenthere is no searched-for pattern in which that character succeeds theprefix that the current node represents). Suppose, for example, that thecurrent node is node 14 in FIG. 9. That node represents the ABEB (whichwill be referred to in the following as the “current prefix”). If thenext input character is D, a failure has occurred, because there is noprefix ABEBD in any of the searched-for patterns.

In some embodiments of the Aho-Corasick algorithm, the node 14 datastructure would nonetheless include an entry corresponding to D, andthat entry would identify the BD-representing node 7, because B is thelongest suffix of the node-14-represented prefix ABEB that D immediatelysucceeds in any searched-for string. Stated more generally, the currentnode's entry corresponding to the next input character would identifythe node that represents the longest prefix that results fromconcatenating the current input character with a suffix of the currentprefix.

Although the foregoing may describe the operation of an instance of theAho-Corasick algorithm in a given scenario wherein a pattern-matchfailure has occurred (that is, when input pattern ABEBD is presented),there are embodiments of the present invention wherein the patternmatching of an input pattern may succeed (or may proceed in an alternatemanner than that presented herein). Suppose, for example, that the nextinput character (in the sequence ABEB) is an A instead of the D asdescribed in the foregoing scenario. An optimization of the Aho-Corasickalgorithm may additionally or alternatively provide node 14 with anentry corresponding to that character (where the entry would represent alink to node 2; noting, however that this is not explicit in FIG. 9), inimplementations of the present invention, there may be two concurrentlyaddressed RAMs (namely, the root-node RAM 806 and the other-node RAM804) and if the current node's data structure in the other-node RAM 804has no entry for the input character, the next address's high-order bitsmay be drawn from root-node RAM 806 rather than from other-node RAM 804.Thus, in implementations, a given node may not need an entry for a givencharacter if that character's entry in the given-node structure wouldrepresent a link to the same node as its entry in the root-nodestructure would. Specifically, since the root-node structure's entry forA identifies node 2 and node-14 structure's would, too, node 14 may notrequire an entry for that node.

In this manner, many other entries may be similarly be dispensed with,and one consequence may be that many of the node data structuresaccording to the present invention may require less memory than otherAho-Corasick embodiments. This may result in lower computationalcomplexity within content search logic 312 and/or associated modules, anoptimization that may provide advantages in cost and/or processing speedand/or in reliability.

Additional optimizations may be realized, as well. To reduce the amountof memory needed to store the node links, some implementations may add amechanism to allow the removal of a portion of the node links when theselinks may be holding the default, root-node-identifying value. In someimplementations, for example, many nodes may use only a small number oflinks to hold node IDs. The rest may, in general, be the default value,which is the root node ID. Thus, in implementations, in order to utilizememory efficiently and in order to maximize the number of nodes inmemory, only the links with node IDs may be stored.

These types of memory optimizations provided by the present inventionmay be seen in the following example implementations. In one suchexample, associated memory space may be divided into sixteen regions ofequal size. Each region may then be programmed to use one of three nodesizes. The node sizes (in this example) are 64 entries, 128 entries, and256 entries. The alignment of the nodes in each region may also beprogrammable. The possible alignments are 0, 64, 128 and 192 entries.The alignment thus maps the reduced node entries in memory into theoriginal node offsets, thereby reducing the memory requirement. In anexample, in this scheme (though others of this type are possibledepending on the context and requirements of a particularimplementation), if the alignment is 64 and the size of the node is 64entries, there will be no entry for a character whose values is in therange 0-63 or 128-255; when the next character has any of those values,the next node ID will be drawn from root-node RAM 806 rather than fromother-node RAM 804. But for entries for character values 64 to 127, thenode ID should still be drawn from root-node RAM 806.

Note that in implementations of this type, the region number 0 may bepermanently set to a node size of 256 entries, and although this may notitself provide optimization, within the present invention, this approachmay provide another optimization.

FIG. 11 provides, without limitation, an example of this approach. Inthis example, on-chip memory is deployed and leaf nodes that have nolinks may be further optimized by eliminating the entire table of links.Since all the links will, in this case, be the default value, the nextnode may be solely determined by an on-chip root lookup. Empty leafnodes will be assigned node IDs that do not map to physical memory.Region number 0 is permanently set to a node size of 256 entries so theE1 and E0 bits are not used in the formulation of the node number. Thus,this configuration may allow an optimization wherein nodes may be mappedwithout using any memory space. When the region number is 0 and the E1bit is 0, the circuit may map the node ID to the appropriate read/writecapable device. In this case, the node ID is used to determine the nextnode. When the region number is 0, the T bit is 1 and the E1 bit is 1,an empty leaf node will be decoded and the next node will be determinedby the on-chip root lookup. The resultant node ID format and SRAM (orsome appropriate read/write capable device) address format is shown inFIG. 11.

Note that empty leaf nodes may only be allowed in region 0. Therefore,an empty leaf node may be decoded whenever the T bit is set and the E1bit is set and the region number is 0. The E1 bit is ignored in regions1 to 15 if they have node sizes of 256 entries. The node ID may bereserved for the root node where the region number is 0, the T bit is 0and the E1 bit is 1. In this instance, this ID composes the root nodefor all trees and an optimization is realized in the use of the on-chiproot lookup to determine the next node.

Note that the format of the node ID for the root node may imply anon-terminal node while the format of the empty leaf node implies aterminal node. In the pattern tree, therefore, an empty leaf node shouldalways be terminal and the root node should never be terminal. Thus,FIG. 11 illustrates, without limitation, an example embodiment wherein apattern tree with a terminal node and an empty leaf node is shown.Terminal node 3 is not empty because it needs to transition to node 2when it detects the character ‘b’ and therefore requires a link to beplaced in its table.

FIG. 12 illustrates a pattern tree with a terminal node and an emptyleaf node, which may be implemented with a root node and 4 nodes in SRAM(or any and all other memory devices). FIG. 13 illustrates, for exampleand without limitation, how such a pattern tree may be embodied in theSRAM (or any and all other memory device). In this example, all thenodes are in a 256-entry region. Each node has 256 links, and each linkconsists of a node ID. If the link is empty (meaning that node does nothave a transition for that particular character offset) its node ID mustbe the root node. (Note that the terminal node 23 does not have itsterminal bits set.) Terminal nodes, in this example, are programmed bysetting the terminal bit on all links to the terminal node. Node 23 isterminal by virtue of the terminal bit being set in a node ID at offset‘a’ in node 22. Similarly, in this example, the empty leaf node 20E is aterminal node because the terminal bit is set at offset ‘c’ in node 21.The node offset of the empty leaf node is not used to calculate the nextnode. When the matching engine 708 encounters an empty leaf node, thenext node will be the root node. The node offset of the empty leaf nodeis still important since it will be used to generate the match eventnumber which indexes into an even translation table which may identifyto subsequent circuitry the rule that the detected match contributes tosatisfying. An embodiment of the event translation table may bedescribed in detail hereinafter or elsewhere.

Referring now to the present invention in general, embodiments mayprovide methods that optimize position dependent string searches. Thus,embodiments of the flow processing facility 102 may include suchposition dependent string searches. In particular, the content searchlogic 312 may comprise and/or support such string searches. It will beappreciated that these string searches (or, indeed, any and all stringsearches) may be implemented in hardware, software, or a combinationthereof.

It is noteworthy that string searches in conventional approaches may beconstrained and/or limited by the position of the string when matchingspecific formats. Among the consequences of these limitations may beperformance degradation and/or increased complexity and/or increasedcost. While string search algorithms that address position constraintsare generally implemented by searching only over the pertinent dataranges, this may not work well for multi-pattern search since eachpattern may have different position ranges. The present inventionaddresses these limitations without impacting performance.

“Position dependent patterns” may be understood to refer to patternsthat provide valid matches only if they occur at a specified positionwithin a packet. “Absolute position patterns” may be understood to referto patterns with position parameters that are measured from thebeginning of the packet. “Relative position patterns” may be understoodto refer to patterns with position parameters that are measured from theend of the previous pattern match.

In one implementation and without limitation, position dependentpatterns may be translated as position independent patterns except thatthe command used in the event translation table may, in this case, bethe TRNS_POS command instead of the TRNS_RULE command. Additionally, anentry in the position events table is needed to specify the patternposition and resultant rule number and sub-rule number. FIG. 14 showsone implementation of the relationship between the tables.

The absolute position pattern may, in this instance, be translated byusing a PCMD_START command in the position events table to specify theposition parameters. To accomplish this, two consecutive entries in theposition events table are reserved for the absolute position pattern.The first entry is then written with the PCMD_START command and theabsolute position data. The second entry is written with the PCMD_DONEcommand along with the rule number and sub-rule number. The offset ofthe first position entry is then written into the event translationtable along with the TRNS_POS command. The event translation table iswritten using the node number as the offset into the table.

The following table shows the values used, in this example, for the lowrange and high range fields in the position event entry for absoluteposition events based on the SNORT language.

Absolute Position Event Entry SNORT Position Low Range Options ValueHigh Range Value none 0xFFFF 0xFFFF offset: N; N 0xFFFF offset: N;depth: M; N M depth: M; 0xFFFF M

Note that the relative position pattern in this instance is translatedwith a PCMD_NEXT command in the position events table to specify theposition parameters. In these methods, relative positioning requires aposition to be established with a match of a pattern. This may beaccomplished with the PCMD_START command. In this case, however, it maynot be necessary for the PCMD_START command to be position dependent.But the pattern must use the position event table to establish theinitial position for the relative position patterns that will follow.The PCMD_NEXT command will be used, in this instance, to specifyrelative position values.

A PCMD_DONE command will indicate the rule number and sub-rule numberfor resultant matches. The following table shows the values used, inthis example, for the low range and high range fields in the positionevent entry for relative position events based on the SNORT language.

Relative Position Event Entry SNORT Position Low Range Options ValueHigh Range Value none 0x0000 0xFFFF distance: N; N 0xFFFF distance: N;within: M; N M Within: M; 0x0000 M

Thus, as shown below, the rule “content: ‘abc’; content: ‘def’;distance: 5; within: 10;” would produce results as may be seen in thefollowing Event Translation Table and Position Event Table. As shown,the string “abc” will have the node number 100 and the string “def” willhave the node number 102.

Event Translation Table Offset More Command Data fields 99 100 1TRNS_POS POS_EVENT_NUMBER = 25 101 0 TRNS_RULE RULE_NUMBER = 71 102 1TRNS_POS POS_EVENT_NUMBER = 26 103 0 TRNS_RULE RULE_NUMBER = 71 104

Position Events Table offset Command Low Range High Range 24 25PCMD_START 0xFFFF 0xFFFF 26 PCMD_NEXT 5 10 27 PCMD_DONE Rule# = 71Sub-rule# = 1 28 29

“Negative patterns” may also be implemented in a position events table,where “negative patterns”, in these implementations, may be understoodto refer to patterns that match only if the pattern is not detectedwithin a specified position range. Negative patterns may require an“anchor pattern” to establish the current position context. In thisexample implementation, the negative pattern is written into theposition events table after the anchor pattern. The PCMD_NEG_NEXTcommand is then used to set the position range in which the pattern isnot expected.

Thus, in the present example, to search for the SNORT options content:“ab”; content:!“cd”; within: 100; the string “ab” is inserted into thepattern with the terminal node at 150 and the string “cd” is insertedinto the pattern tree with the terminal node at 160. Both strings maygenerate position events from the event translation table which in turn,invokes commands in the position events table.

The following Event Translation Table and Position Event Tableillustrate this example.

Event Translation Table Offset More Command Data fields 149 150 1TRNS_POS POS_EVENT_NUMBER = 133 151 0 TRNS_RULE RULE_NUMBER = 50 160 1TRNS_POS POS_EVENT_NUMBER = 134 161 0 TRNS_RULE RULE_NUMBER = 50 162

Position Events Table offset Command Low Range High Range 132 133PCMD_START 0xFFFF 0xFFFF 134 PCMD_NEG_NEXT 0xFFFF 100 135 PCMD_DONERule# = 50 Sub-rule# = 1 136

As shown above, the string “ab” will, in this example, generate a matchevent with the node number 150. The node number translates to a TRNS_POScommand with a position number of 133. The position number is used toinvoke the PCMD_START command at offset 133 in the position eventstable. The PCMD_START command will check for a valid position and thenload the next command into the position context.

Thus, in the present example, the position context now contains theposition of the string “ab” and the command of the negative pattern. Ifthe string “cd” is detected and generates a match event, the match eventwill produce a TRNS_POS command that will invoke the PCMD_NEG_NEXTcommand at offset 134.

The position, in this example, is checked against the stored positionand range. The PCMD_NEG_NEXT command will clear the position context ifthe position of the string “cd” is within the 100 character range. Ifthe 100 character position range is reached without detecting the string“cd”, the next command is retrieved and executed. In this case, the nextcommand is a PCMD_DONE command that generates a rule match event.

The teachings of the present invention may also be applied to processing“regular expressions”. A “regular expression” may be understood to referto a representation of a pattern that may have a variable length and maypossibly have many alternate forms. In conventional approaches,searching for regular expression patterns may often require intensiveprocessing power and memory since conventional algorithms, gearedtowards searching for a single expression, may not scale well incomputational terms when attempting to search for many expressionssimultaneously. In addition, in these approaches, memory utilization mayalso be a problem since these memory requirements may increaseexponentially as the number search expressions increases.

The present invention provides methods to search for many expressionssimultaneously, addressing situations wherein memory utilizationincreases linearly with the number of expressions and/or wherein theremay be untenable increases in computation complexity. Note that, as inthe foregoing examples, while these methods may be illustrated in thefollowing example implementations as applied in hardware, suchimplementations may be accomplished through a number of means (seeparagraph 91 above).

A regular expression may be understood in the following examples tocompose a text string that may include “metacharacters” to describecomplex patterns. “Metacharacters” may be understood in the followingexamples to compose ASCII characters that may be reserved for specifyingpattern features. Note that in some implementations, these reservedASCII characters can still be used in other contexts via an escapesequence.) Note also that regular expressions are by conventiontypically delimited by the slash character, and this convention isadopted in the following examples.

The metacharacters used in the following examples are outlined in thefollowing table.

Metacharacter Description α Escape-A reserved character is inserted bythe escape sequence of the backslash followed by the reserved character.In an example, the backslash is specified with the sequence “αα”. |Alternation ( ) Grouping { } Quantification [ ] character class{circumflex over ( )} beginning of string [{circumflex over ( )} When itis the first character in a character class it negates the characterclass $ end of the string * matches 0 or more of the previous group orcharacter + matches 1 or more of the previous group or character ?matches 0 or 1 of the previous group or character . matches anycharacter except the new-line character

Within the present teachings, two basic features that may characterizeregular expressions are “alternation” and “quantification”.“Alternation” may be understood in the following examples to refer tothe capability to specify alternate strings or characters. Alternationmay be seen as equivalent to using multiple strings, but in theseembodiments may be much more compact. Thus, the regular expression/(get|set)value/ may be, in the present example, equivalent to thestring “setvalue” and “getvalue”, and thus, matching the regularexpression is the same as matching either string.

“Quantification” may be understood in the following examples to describea repetitious pattern. The number of repetitions may be any integervalue greater than 0 and is functionally unlimited.

Regular expressions may also, in the present invention, define someuseful non-character string attributes, such as the beginning of thestring and/or the end of the string. When the regular expressionfeatures are combined, they may provide a flexible and compact methodfor describing complex patterns.

Some embodiments of the present invention that detect patterns specifiedby regular expression may or may not support some or allregular-expression constructs. An embodiment that is providedhereinafter for the purposes of illustration and not limitation may notsupport the following regular-expression features:

Greedy Matches

Lookahead Assertion

Lookbehind Assertion

Backreferences

\b and \B—matches word boundaries

Nested quantifiers (such as /(c(ab){2,}){3,}/

However, such exclusions of certain regular-expression features from anyand all examples of the present invention, whether those examples areprovided herein or elsewhere, should not be construed to limit the scopeof the teachings contained herein. It will be appreciated that any orall regular-expression constructs may or may not be supported by a givenembodiment of the present invention, and that all such embodiments ofthe present invention are within the scope of the present disclosure.

The following example embodiment describes how each regular expressionconstruct may be implemented using one type of pattern-detectioncircuitry.

Regular expressions may be implemented in this example pattern-detectioncircuitry by partitioning the regular expression into a set of positiondependent substrings that are equivalent to the regular expression. Byvirtue of this method, searching for regular expressions in thepattern-detection circuitry will then consist of searching for theposition dependent substrings. The associated pattern tree may then beused to hold and to search the substrings of the regular expression.

In addition, in this example, a “character class detector” may be usedto detect the character class and wildcard constructs in subject regularexpressions. The “character class detector” may be understood in thefollowing examples to compose a logic function that detects “characterclass strings.” A character class may be understood in the followingexamples to specify a set of byte values that will produce a match and,in the present example, is delimited with square brackets. In anexample, the character class /[abc]/ will match the characters ‘a’ or‘b’ or ‘c’.

A “negative character class” may be specified in the following exampleswith the caret character as the first character inside the squarebrackets. Thus, for example, the expression /[̂abc]/ will match any 8-bitvalue except for the characters ‘a’, ‘b’ or ‘c’.

In this example, when the character class detector matches a characterclass, it may monitor the data stream to determine the string with themost consecutive matches. When the character class string ends, thecharacter class event number along with its position may be sent to a“correlation block” for further processing.

All substring patterns other than character classes, in the presentexample, may be are stored in the pattern tree in SRAM (or otherread/write capable device.) These substring patterns may be searched bytraversing the tree to find the terminal nodes. When a terminal node isencountered, the match event along with its position may be sent to the“correlation block” for processing.

The “correlation block” may be understood in the following examples tocompose a function that may validate the position of the patternsrelative to each other. In this example, some or all position parametersmay be held on-chip and used to verify patterns as they occur. In thismanner, the relative position context may be maintained so that nextpattern can be correctly validated. In variations of this example,correlation may also aggregate multiple pattern matches into rulematches.

In the following examples, alternation is specified in regularexpressions with the ‘|’ character. The regular expression /abc|def/will thus match the strings “abc” and “def”. In the following examples,in order to implement the expression /abc|def/ in the pattern-detectioncircuitry, the strings “abc” and “def” are both inserted into thepattern tree. A match of either string will result in a match of theexpression /abc|def/.

Alternation of character may also be achieved, in this exampleimplementation, with the character class denoted by square bracketgroupings. In an example, the regular expression /[abc]/ matches thestrings “a”, “b” and “c”. But note that methods using character classmay be implemented in one of two ways. In the first method, the patterntree may used to encode all the possible matches. In this method, inorder to implement the expression /[abc]/, a node may be created foreach character, and the character link to each node is added. Each nodemay also have its terminal bit set to indicate the match of the pattern.Thus, a match may result when any of the characters in the characterclass are encountered.

The second method may use the character class detector block. In thisapproach, the character class detector scans for standard characterclasses and, in this implementation, for up to 32 user defined characterclasses. But note that since the character class detector may be alimited resource, implementations that use this method typically dealwith complicated patterns.

In this sense, the two methods for implementing alternation of usingcharacter class may be combined in some embodiments of the invention,and thus, their respective methods should not be viewed as exclusive ofone another.

In the following example implementations, quantification may bespecified with quantifiers using the ‘*’, ‘+’, ‘?’ or ‘{ }’metacharacters. The quantifiers, in this case, indicate the number oftimes the previous character or group is to be consecutively matched. Insome variations, however, the use of quantifiers may result in anexpression that can be matched at different lengths. The matching of thelongest pattern is called, in these example implementations, a “greedy”match, whereas by contrast, a “non-greedy” match will find the shortpattern that matches the expression. The following table shows the liststhe greedy and non-greedy quantifiers.

Greedy Non-greedy quantifier quantifier * *? Match 0 or more + +? Match1 or more ? ?? Match 0 or 1 {x, y} {x, y}? Match a minimum of x and amaximum of y {x,} {x,}? Match a minimum of x and unlimited maximum {x}{x}? Match exactly x

By convention, and example implementations follow this convention, thefollowing greedy matches compose the default in regular expressions. Tospecify a non-greedy match, a question mark is added to the quantifier.Note that the pattern-detection circuitry in this example may notsupport greedy matches, but that greedy quantifiers may be implementedas non-greedy quantifiers. Other implementations may support greedymatches, however, and the following examples are presented without lossgenerality to such cases.

Further, in the following examples, an asterisk may indicate a matchstring consisting of 0 or more instances of the previous character orgroup. In an example, /a*/ specifies a string of 0 or more a's. Thestrings “ ”, “a”, “aaa” and “aaaaaaa” all match the regular expression/a*/. In addition, the regular expression /.*/ represents a string of 0or more characters without any regard for the value of the character.

In the following examples, the plus sign indicates a match stringconsisting of 1 or more instances of the previous character or group.Thus, the strings “a”, “aaa” and “aaaaaaa” all match the regularexpression /a+/ but the string “ ” does not match.

A more general form of quantifiers may, in the examples that follow, usethe curly brackets in the form of/{x, y}/. X indicates the minimumnumber of iterations and y indicates the maximum number of iterations.If y is not present, the maximum number of iterations is infinity.Therefore, /a*/ can also be expressed as /a{0,}/ and /a+/ can also beexpressed as /a{1,}/.

In the following example implementation, when the /.*/ construct isencountered, the regular expression may be divided into threesubstrings. In these cases, the string before the /.*/ becomes asubstring and the string after the /.*/ becomes a second substring. Athird string may be formed by combining the two substrings. Thus, theexpression /abc.*def/ will produce the substrings “abc”, “def” and“abcdef”. Searching for the expression /abc.*def/ will, in theseexamples, consist of searching for the string “abc” and then searchingfor the “def” (i.e. the second string has a relative position and mustbe detect after the first).

The /a?/ construct may, in the following examples, produce two strings.The question mark quantifier in this case specifies a match of zero orone, so the two strings created are the null string and “a”. In anexample, the expression /abc?de/ is found by searching the string “abde”or “abcde”.

In the following example implementation, the /a*/ expression producestwo strings. In the first string, the construct may be removed becauseit is replaced with the null string. In the second string, the constructmay be replaced with /a+/. Thus, searching for the expression /abc*de/will now consist of searching for the strings “abde” and /abc+de/.

The methods embodied in following example implementation use a characterclass to search for the ‘c+’. The ‘c+’ expression is replaced with thecharacter class [c]+ which may be found by the character class detector.The character class detector may return the longest string ofconsecutive character classes. The expression /abc+de/ is then found bysearching for the string “ab”, then searching for the character classstring [c]+ and then searching for the string “de”. The expression maybe matched if the example correlation module verifies that each stringoccurs right after the previous string.

In the example implementation that follows, processing of thequantifiers ‘{x,y}’ and ‘{x,}’ is executed in a manner similar to thatapplied to the quantifier ‘+’. The difference, in this case, is that theposition limits are encoded into the position rules and verified in thecorrelation module.

In cases where an exact number of matches are needed as specified by thequantifier ‘{x}’, the following example implementation unwinds theexpression and then transforms it with the previous describedprocedures. Thus, the expression /a {8}/ will produce the string“aaaaaaaa”.

Negative character classes are supported, in the following exampleimplementations, by means of a character class detector. The negativecharacter class is specified, in this case, with the caret character asthe first character in a character class. In an example, the expression/[̂abc]/ will match any character except for the characters ‘a’, ‘b’ or‘c’.

In some implementations, efficiencies may be achieved within theteachings of the present invention by combining alternation andquantification to create complex patterns. The same partitioningprocedures may be used when both alternation and quantification are usedin the same expression. Thus, in these cases, to search for theexpression /a*(cbd|c*|d.*)e/, two expressions may be derived/(cbd|c*|d.*)e/ and /a+(cbd|c*|d.*)e/ by expanding /a*/. Thentransformations may be applied to both expressions. In this manner, theexpression /(cbd|c*|d.*)e/ may produce the substrings “cbde”, /c*e/ and/d.*e/, and the expression /a+(cbd|c*|d.*)e/ may produce the substrings/a+cbde/, /a+c*e/ and /a+d.*e/. But, in implementations that deploythese methods, the expression /c*e/ may be replaced “e” and /c+e/, andthe string /a+c*e/ may produce /a+e/ and /a+c+e/. The string /d.*e/ maythen be divided into 2 strings “d” and “e” where the second string mustbe detected after the first. The expression /a+d.*e/ may be divided intothe strings /a+d/ and “e” and the second string must be detected afterthe first. Finally, the character iterations may be replaced with thecharacter class.

It can be seen that, in this example implementation, matching any of thesubstrings is equivalent to matching the regular expression. Theresultant substrings are:

“cde”

/[c]+e/

“e”

“de”

“d”, “e”

/[a]+cbde/

/[a]+e/

/[a]+[c]+e/

/[a]+de/

/[a]+d/, “e”

Some implementations of the present invention may support positivelookahead and lookbehind functionality. When present, the positiveassertion may be specified with /(?=)/ and the negative lookaheadassertion may be specified with /(?!)/. In practice, these assertionshave zero width which means that the matching of the enclosed expressiondoes not move the character pointer used to maintain the currentcharacter to process. Thus, the expression preceding the positivelookahead assertion will match only if the lookahead expression matches,and the expression preceding a negative lookahead assertion will matchonly if the lookahead expression does not match. In an example, inimplementations that support lookahead, the expression /foo(?=bar)/matches the string “foo” if the proceeding characters are “bar”. In thiscase, a match would result in a character pointer that points to the bas the next character.

Though there may be implementations that do not support the lookaheadassertion, there are nonetheless four rules in the SNORT rule base thatmay use the lookahead assertion. Two of these rules use the lookaheadfor optimization rather than pattern description.

The positive lookbehind assertion may be specified in someimplementations with /(?<=)/ and the negative lookbehind assertion isspecified with /(?<!)/. In a positive lookbehind assertion theproceeding expression matches only if the lookbehind expression matches.In a negative lookbehind assertion the proceeding expression matchesonly if the lookbehind expression does not match. In an example, theexpression (?<!foo)bar matches the string bar if the preceding 3characters are not foo.

In the present example implementation, the ‘̂’(caret) is also a “zerowith” assertion, and may be used to match the beginning of the string,and/or the beginning of the packet. The ‘̂’ may be implemented in thepresent example pattern-detection circuitry by starting the packet scanat an initial root node in the pattern tree that implicitly matched thebeginning of the packet. After comparing the first character, theinitial root state is no longer used and will only be entered again atthe beginning of the next packet. The initial root node, then, isdifferent from the root node of the pattern tree, and the root node ofthe pattern tree implies no matches, and thus, can be entered at anybyte within the packet. In an example, in the example implementation,the expression /̂apple/ may produce a search string of “apple”. Thestring “apple” may link, in this case, to the initial root node but notthe root node. Therefore, the string “apple” can be matched at thebeginning of the packet but will not match after the beginning of thepacket since the root node is not linked to the string “apple.”

In the example implementation, the $ metacharacter matches the end ofthe string and/or the end of the packet. In the present example, thismay be implemented within the pattern-detection circuitry in theEOP_registers. A range of node ID can be reserved to additionally checkfor the end of the packet. In this instance, the EOP_register may beprogrammed with a base number and an offset. If the node ID is withinthe range indicated by the EOP register, a match event may be generatedif the current character is the last character of the packet. In anexample, the expression /apple$/ produces the string “apple.” The nodeID at the ‘e’ of the string will be assigned a value that is within therange of the EOP registers, and the match of the string “apple” willactivate the EOP check. If the character ‘e’ is the last character ofthe packet, a match event is generated with the adjacent node ID (i.e.node ID=node ID of ‘e’+1).

In the example implementation, modifiers may be used in regularexpressions to control the interpretation of some features. Modifiersare located at the end of the expression after the slash and may berepresented with the letters i, m, s and x. In the example expression/apple/is, the i and s modifier is specified.

When the s modifier is present, the dot metacharacter in the expressionwill match the newline character. If the s modifier is not present, thedot metacharacter would match the newline. Without the s modifier, thedot metacharacter may be replaced with the character class [̂\n].

The i modifier will cause the expression to be case insensitive.Otherwise the expression is case sensitive.

The m modifier is used in this example implementation to treat thesearch text as multiple lines. By default, the search text is consideredone long string. The ‘̂’ metacharacter will match the beginning of thesearch text and the ‘$’ metacharacter will match the end of the searchtext. If the m modifier is present, the ‘̂’ metacharacter will match thebeginning of the search text and also immediately after a newlinecharacter. With the m modifier present, the ‘$’ metacharacter will matchthe end of the search text and also immediately before a newlinecharacter.

The m modifier may be implemented in the example pattern-detectioncircuitry by inserting a newline at the beginning of the expression ifthe ‘̂’ character is used. The resultant string would then be linked tothe root node of the pattern tree, and the initial root node will belinked to the second character of the expression to bypass the newlinethat was inserted. Thus, the expression /̂apple/m will produce the string“\napple”, the root node will link to the newline character and theinitial root node will link to the ‘a’ character. In this manner, theexpression /̂apple/ will be matched if the string “apple” appears at thebeginning of the search text or at the beginning of a line which wasterminated by the newline. The example pattern-detection circuitry mayimplement the m modified ‘$’ metacharacter by appending a newline to theend of the string. The string can now be matched by matching the newlineand/or to the end of the packet. Thus, in this case, the expression/apple$/m will produce the string “apple\n” where the node ID of the ‘e’is assigned to a value within the range of the EOP registers. Theexpression /apple\n/m will match the string “apple” if it occurs justbefore a newline or at the end of the search text.

In the example implementation, the x modifier may cause all white spacecharacters in the expression to be ignored except when it is escaped orif it is in a character class. This modifier may only affect theinterpretation of characters and can be implemented in the regularexpression compiler.

“Backreferences” use groupings that have already been matched to specifyan expression. In an example, in the example implementation, whenprocessing the expression /(a|b|c)d\1/, the ‘\1’ will match the stringthat the grouping “(a|b|c)” matched. The strings the expression/(a|b|c)d\1/ will match are “ada”, “bdb” and “cdc”. It will not matchstrings “adc” or “bdc” or “cda”. Backreferences may not be supported inall implementations, but in the SNORT 2.3.0 rule set there are 242 rulesrelated to Oracle applications that use backreferences.

In the example implementation, the U modifier may be used to apply aregular expression to a decoded URL string. The R modifier may changethe starting position of the regular expression search to the end of thelast matched pattern. The B modifier may select the undecoded data foruse in the regular expression search.

The present invention may extend these optimizations to situationsrequiring the matching of the beginning of a string. In the exampleimplementation, the “caret” character at the beginning of a regularexpression matches the beginning of the string or packet. In the presentexample, this may be implemented with an initial root node in thepattern tree. The initial root node has a pre-defined number and scansmay start at the initial node.

To illustrate the use of the initial root node, the example from FIG. 9is used. The patterns AABA, ABEBE, ABF, BEBC, BEBB and BDD are to beinserted into the pattern tree, but, in this case, the AABA pattern ischanged to /̂AABA/. FIG. 15 shows the resultant tree. Thus, as describedin the foregoing, packet scans start at the initial root node buteventually transition to the main tree. The pattern AABA is matched atthe terminal node 12 but can only get there from the initial root node.

If, however, the m modifier is not indicated, the “caret anchored”strings are inserted onto the tree at the initial root node, and whenmerging, nodes in the initial tree are not considered when selectingprefix nodes. In addition, all nodes in the initial tree are merged. Itis evident that method is different from merging of the main tree inthat in the latter case, only nodes that have a depth greater than oneare merged, and the nodes in the initial tree are never merged withother nodes.

Now in the present example, again referencing FIG. 15, if the expressionis changed to /̂AABA/m, the caret must match both the beginning of thepacket and the newline. Thus FIG. 16 shows the pattern tree with theexpression /̂AABA/m. Note that there are now two ways to get to theterminal node 12. One path starts at the initial root node while theother starts at the root node. If the pattern AABA is not at thebeginning of the packet (i.e. start at the initial root node) then it isonly matched if it follows a newline character.

If the m modifier is indicated, the caret anchored string is prependedwith a newline and then inserted into the tree at the root node. A linkis then made from the initial root node to the representing the match ofthe first character and the newline.

To demonstrate how all patterns are inserted, the new pattern /̂AC/ isadded. The pattern tree in FIG. 17 contains the patterns /̂AC/, /̂AABA/m,ABEBE, ABF, BEBC, BEBB and BDD. Notice that because the first characterof the two regular expressions is the same, they are merged, in thisexample, by copying the transitions from node 17 to node 19.

In one example implementation, the following steps may be used to buildthe pattern tree with caret anchored strings.

-   -   1—Insert regular patterns starting at the root node to form the        main tree.    -   2—Insert m-modified caret anchored patterns with pre-pended        newlines starting at the root node and add link to initial root        node. Keep track of the patterns that have implicit newlines        since they will be treated differently than patterns that have        explicit newlines.    -   3—Insert caret anchored patterns to form the initial tree—if        there are overlaps with patterns that have implicit newlines,        propagate the link between the initial root node and the node        representing the first character of the pattern with an implicit        newline onto the next node in the initial tree. On the initial        tree, links to other nodes in the initial tree have precedence.        A link to the rooted tree is ignored if that link character is        already used for a link to the initial tree. A link to the        initial tree will overwrite a link to the rooted tree.    -   4—Merge all patterns—when merging the initial tree, implicit        newlines are ignored (i.e. when comparing prefixes, the implicit        newline is removed before the comparison).

The present invention may extend these optimizations to situationsrequiring the matching of the end of a string. In the exampleimplementation, the ‘$’ or end of the string may be matched with the EOPregisters. The EOP register defines a base number for a range of nodenumbers that will be reserved for end of string matching. The EOP MaskRegister defines the size of the range of node numbers. The noderepresenting the last character in a pattern that matches the end of thestring may be assigned a node number in the range defined by the EOPregister and EOP Mask register.

In the example implementation, for example and without limitation, theEOP register may be programmed to 0x0100 and the EOP Mask Register maybe programmed to 0x0003. This specifies a range of 16 node numbers thatwill be reserved for the end of string matching. When presented thepattern /AABA$/, the string “AABA” will be inserted into the patterntree. Then the terminal node of the string may be assigned to nodenumber 0x0101. If the node number 0x0101 is matched, the next characteris checked to see if the end of the packet has been reached. If anothercharacter exists (current character is not the last), the event isdiscarded. If there are no more characters (meaning the packet hasended), a match event is generated using the node number 0x0102 (i.e.0x0001+1).

In this example, the translation commands for this pattern may be loadedinto offset 0x0102 of the event translation table. If the pattern is/AABA$/m, then the string “AABA\n” may be inserted into the patterntree, and the node representing the last A is designated a terminator,and that node may be assigned node number 0x0101. Likewise, in thisexample, an event with node number 0x0102 may be generated if nodenumber 0x0101 is detected at the end of the packet. The eventtranslation table may then be written with the translation commands atoffset of 0x0102. The node representing the newline is thus, also aterminal node. This node can be arbitrarily assigned, but itscorresponding location in the event translation table will contain alink to the entry at 0x0102. Therefore, detecting the string at the endof the packet or at the end of a line will, in this exampleimplementation, execute the same translation commands.

The present invention may also extend these optimizations to expressionswith alternations are implemented by either inserting all possiblecombinations of patterns into the pattern tree or by using the characterclass detector.

In the example implementation, when patterns of the form(/(pattern1|pattern2), are encountered, each pattern in the alternationgroup may be inserted into the pattern tree. The translation of eachpattern will produce the same rule number and sub-rule number.Therefore, a match resulting from either pattern will produce the sameresult. When alternation is used in series, the patterns may, in theexample implementation, be unwound to obtain a set of equivalentpatterns. In an example, using this method, the pattern /(ab|cd)(badc)/will generate the patterns “abba”, “abdc”, “cdba” and “cddc”. All of thegenerated patterns may then be inserted into the pattern tree and theirtranslation commands programmed to produce the same result.

Likewise, when patterns of the form (/(char1|char2)/) are encountered,alternation of characters can be employed by means of the pattern treeand/or with the character class detector. To implement this method usingthe pattern tree, each alternate character would produce a string thatis inserted into the pattern tree. In an example, the expression/(a|b|c)/ would generate the strings “a”, “b” and “c”. The expression/new(a|b|c)/ would generate the strings “newa”, “newb”, newc”. Thismethod may be preferred in situations where the number of characters isrelatively small (less than 5).

When patterns of the form (/[characters]/) are presented, a characterclass may be considered functionally equivalent to alternation of singlecharacters. If there are many alternate characters in an expression, thecharacter class may be chosen since it may implement the expression moreefficiently. The character class may be implemented by assigning a“character class number” to the expression. Each character in thecharacter class may then be used as an offset into the “character classtable” in order to set a bit representing that character. When any ofthe characters are detected, a character class event is sent to theposition context. Character class methods may have an anchor stringwhich establishes the position in the position context and which mayalso trigger the character class. Such a trigger may enable the selectedcharacter class in the character class detector. In the exampleimplementation, the CC command for this expression would have both thelow range and high range values set to 0xFFFF and the NEGATE bit may becleared.

When patterns of the form (/[̂characters]/) are encountered, the negativecharacter class may be handled in the same manner as the character classexcept that the character values used for detection may be the valuesnot listed in the square brackets. In an example, the expression /[̂abc]/matches every character value except for ‘a’, ‘b’ and ‘c’.

The present invention may extend its teachings to matching repetitivepatterns. Patterns larger than one character that are repeated usingquantifiers may be implemented by unwinding the repetition and insertingthe resulting patterns into the pattern tree. The following scenariosare possible.

-   -   a. /(pattern1){x}/—The expression /(abc){3}/ may be unwound to        the string “abcabcabc”. The node representing the last character        may be the terminal node.    -   b. /(pattern1){x,}/—In this case, the pattern may be unwound to        at least the minimum value x+1. The links can then be looped        back to repeat the pattern. For instance, in the present example        implementation, the expression /(abc){3,} may unwind to the        string “abcabcabcabc”. The link at the last character will loop        back to the last ‘b’ upon detecting an ‘a’. But note that the        minimum unwound pattern must not be a subset of another pattern,        and the unwinding should continue until the pattern is no longer        a subset of another pattern. If this type of quantifier occurs        at the end of the regular expression, in the example        implementation, it may be reduced to {x} because continuing to        search beyond x matches does not change the match status. It        may, however, affect the final position of the match. In an        example, in the example implementation, the expression        /(ab){2,}/ may actually be implemented as /(ab){2}/, and when        searching the string “abababababac”, will produce a match at the        position of the second ‘b’. But, the match position should have        been the last ‘b’ character. If there are no more position        dependent patterns after the regular expression such as a string        test command, the match position is not used and hence does not        affect the search.    -   c. /(pattern1){x,y}/—In the example implementation, this pattern        must be unwound y times. Thus, the expression /(abc){3,5}/        unwinds to the string “abcabcabcabcabc”. This type of quantifier        may also be reduced to {x} when encountered at the end of the        regular expression. Therefore the expression /(abc){3,5}/ may be        implemented as /(abc){3}/.    -   d. /(pattern1|pattern2){x}/—In the example implementation,        repeating patterns with alternation may also implemented by        unwinding the patterns. In this case, multiple patterns are        generated because each repetition causes a replication due the        alternation. The expression /(abc|def){3}/ unwinds to 8 strings        “abcabcabc”, “abcdefabc”, abcdefdef”, “defabcabc”, defdefabc”,        “defdefdef”, defabcdef”, “abcabcdef”.    -   e. /(pattern1|pattern2){x,}/—In the example implementation, the        patterns in this case may also unwound at least x+1 times, and        links are added in order to loop back to the last pattern. The        difference, in the example implementation, is that in this case,        the link must be made to a node that matches the last pattern.        The patterns in this case cannot be a subset of other patterns        in the tree. This type of quantifier may also be reduced to {x}        when encountered at the end of the regular expression. Therefore        the expression /(abc|def){3,5}/ may be implemented as        /(abc|def){3}/.    -   f. /(pattern1|pattern2){x,y}/—In the example implementation, the        patterns in this expression may be unwound exactly y times and        may thus produce multiple patterns because of the alternation.        When this quantifier is used at the end of the regular        expression, it may be reduced to {x}. Thus, for example, the        expression /(abc|def){3,5}/ may implemented as /(abc|def){3}/.

Note that combining repetitive patterns with other patterns may affectthe links at the node representing the end of the strings.

In the example implementation, when matching the wildcard (dot)character repetitively with the s modifier, the patterns may be treatedas position dependent strings. The patterns may be inserted into thepattern tree and then associated with relative positions stored in theposition events table. In these instances, the following scenarios arepossible:

-   -   a. /.{x}/s—In the example implementation, the expression        (/abc.{x} def/s) may be translated into the string “abc” and        “def”. The string “abc” may be configured to a generate position        event from the event translation table. The position event will        invoke a PCMD_START command with all ones in the range field        (indicating no range limits). The string “def” may be configured        to generate a position event that will execute the command        following the PCMD_START command. That location is programmed        with a PCMD_NEXT command and both range fields are set to x. It        is evident that this is functionally equivalent to relative        positioning of strings using content options such as        “distance:x; within:x;”.    -   b. /.{x,}/s—In the example implementation, the expression        (/abc.{x,}def/s) may be translated into the string “abc” and        “def”. The string “abc” may be configured to generate a position        event from the event translation table. The position event will        invoke a PCMD_START command with all ones in the range field        (indicating no range limits). The string “def” may be configured        to generate a position event that will execute the command        following the PCMD_START command. That location is programmed        with a PCMD_NEXT command and both low range field is set to x        and the high range field is all ones. It is evident that this is        functionally equivalent to relative positioning of strings using        content options such as “distance:x;”.    -   c. /.{x,y}/s—In the example implementation, the expression        (/abc.{x,y} def/s) may be translated into the string “abc” and        “def”. The string “abc” may be configured to generate a position        event from the event translation table. The position event will        invoke a PCMD_START command with all ones in the range field        (indicating no range limits). The string “def” may be configured        to generate a position event that will execute the command        following the PCMD_START command. That location is programmed        with a PCMD_NEXT command and both low range field is set to x        and the high range field is set to y. It is evident that this is        functionally equivalent to relative positioning of strings using        content options such as “distance:x; within:y;.

In the example implementation, when matching the wildcard (dot)character repetitively without the s modifier, the dot may be replacedwith the negative character class [̂\n] and the patterns will produceposition dependent strings. The patterns may be inserted into thepattern tree and then associated with relative positions stored in theposition events table. A character class trigger may be invoked when thewildcard is to be matched. In these instances, the following scenariosare possible:

-   -   a. /.{x}/ translates to /[̂\n]{x}/—In the example implementation,        this may be implemented with the character class detector via a        PCMD_CC command in the position events table. The PCMD_CC        command may be programmed with x in the MIN and MAX field. A        match occurs if a character class event for [̂\n] is not detected        before processing MAX number of characters. The search may be        terminated if a character class event for [̂\n] is detected        before processing MIN number of characters.    -   b. /.{x,}/ translates to /[̂\n]{x,}/—In the example        implementation, this may be implemented with the character class        detector via a PCMD_CC command in the position events table. The        CC command may be programmed with x in the MIN and MAX field. A        match occurs if a character class event for [̂\n] is not detected        before processing MAX number of characters. The search may be        terminated if a character class event for [̂\n] is detected        before processing MIN number of characters.    -   c. /.{x,y}/ translates to /[̂\n]{x,y}/—In the example        implementation, this may be implemented with the character class        detector via a PCMD_CC command in the position events table. The        PCMD_CC command will be programmed with x in the MIN and y in        the MAX field. A match occurs if a character class event for        [̂\n] is not detected before processing MIN number of characters.        The search may be terminated if a character class event for [̂\n]        is detected before processing MIN number of characters.

In the example implementation, if a single character is repeated using aquantifier, the expression can be implemented by unwinding it. Thismethod may be seen as being similar to using quantifiers on patternsexcept that in this case, the patterns are single characters. In anexample, in the example implementation, the expression /a{3}/ may beunwound to the string “aaa”. The expression /a{2,}/ may be unwound tothe string “aaaa” where a link is used to loop back to the repeatedcharacter. The same restriction may apply when the quantifier appears atthe end of the regular expression. Thus, the expression /a{2,}/ may bereduced to /a{2}/.

In the example implementation, a character class requires that an anchorstring or anchor pattern precede it, and this is due to the need totrigger character classes. The position context needs an anchor patternto establish the position context and to trigger the character class.Examples of expressions that violate this requirement may include/\w+\s/, /[̂\n]{3,}\s/ or /\d+\s/. But note that a character class may beallowed at the beginning of the expression if it can be unwound. In anexample, the expression /\sGET/ may be unwound to produce the strings“GET” and “\tGET”.

In the example implementation, a character class may be instantiated byplacing a PCMD_CC command after an anchor pattern in the position eventstable. The PCMD_CC command parameters may be used to specify the validranges for detecting character class events. When the anchor pattern isdetected, the command for the anchor pattern is invoked and will alsotrigger the character class. The position context may, in thisimplementation wait for a character class event until MAX position isreached.

In the example implementation, examples of the patterns in thesescenarios may include:

-   -   a. /[characters]{x}/—In the example implementation in, order to        implement this quantifier, the character class detector may load        [characters] (i.e. the set of byte values that are not included        in [characters]) in the character class table. The PCMD_CC        command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to x.    -   b. /[characters]{x,}/—In the example implementation, in order to        implement this quantifier, the character class detector may load        [characters] (i.e. the set of byte values that are not included        in [characters]) in the character class table. The PCMD_CC        command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to 0x0FFF.    -   c. /[characters]{x,y}/—In the example implementation, in order        to implement this quantifier, the character class detector may        load [characters] (i.e. the set of byte values that are not        included in [characters]) in the character class table. The        PCMD_CC command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to y.    -   d. /[̂characters]{x}/—In the example implementation, in order to        implement this quantifier, the character class detector may load        [characters] (i.e. the set of byte values that are not included        in [characters]) in the character class table. The PCMD_CC        command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to y.    -   e. /[̂characters]{x,}/—In the example implementation, in order to        implement this quantifier, the character class detector may load        [characters] (i.e. the set of byte values that are not included        in [characters]) in the character class table. The PCMD_CC        command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to 0x0FFF.    -   f. /[̂characters]{x,y}/—In the example implementation, in order        to implement this quantifier, the character class detector may        load [characters] (i.e. the set of byte values that are not        included in [characters]) in the character class table. The        PCMD_CC command may then inserted in the position events table        immediately after the anchor pattern and set min position to x        and max pos to y.

Note that the minimum and maximum fields in the PCMD_CC command may inthis example implementation, limit the quantifiers to 4000 or less.

Likewise, in the example implementation, the negative character classmay also use the character class detector. The expression /name[̂\n]{x}/may be implemented by partitioning the string into the pattern “name:”and the character class [̂\n]{x}. The pattern “name:” will generate aposition event that will establish the position context for thecharacter class, and the position event invokes the PCMD_START command.After processing the PCMD_START command, the next command may beread-this will be will be a PCMD_CC command which initiates a characterclass trigger and specifies the limits x and y. When the character classstring ends, a character class event may also generated (that is, whenone of the characters inside the brackets is detected). The characterclass event may be compared with the limits x and y, and if thecharacter class event is within the position limits, the next commandmay then be read and executed. If a character class event is notdetected when the current position reaches the maximum limit, a match isassumed and position context is updated with the maximum limit. The nextcommand is then read and executed.

The teachings of the present invention demonstrate that the inspectionprocess ultimately leads to various actions that may need to be taken inresponse to the contents of data flows 444. Such actions are describedabove in the foregoing and may include (but may not be limited to)generating reports, preventing certain segments of the flow from beingforwarded, and so on.

Note, however, that whether the system takes a particular action may inimplementations, depend on a corresponding action rule 450, (where theaction rule 450 may be described hereinabove or elsewhere, and may referto criteria that the system applies to the contents of flow segmentswithin data flow 444 in order to determine whether to take one or morecorresponding actions).

In the context of the teachings of the present invention, an action rule450 may be considered to have one or both of the following two rulecomponents: 1) a header rule 452 which describes an aspect of a headersuch as protocol type, source address, destination address, source port,destination port, TCP direction, and so forth; and 2) a content rule454, which relates to a payload. Although the action rules 450 may, inthe present description, be applied to in response to individualpackets, the determination as to whether a packet satisfies a given rulemay additionally depend not only on that packet's contents but also onthose of other packets. Note also that in the example implementation,every action rule 450 includes a header rule 452, but not all actionrules include content rules 454.

Note also that the mechanisms that the detection system may use todetermine whether a header rule 452 is satisfied may, inimplementations, differ from that used to make the correspondingdetermination for a content rule 454.

One teaching of the present invention provides efficiencies in the meansby which components that make the packet-rule determination communicatetheir results, and in the example implementation, the high-speedcircuitry provides this computational economy. But achievement of thisgoal may be complicated by the fact that there may be a large number ofrules and by the fact that a given packet's headers and/or payloads maysatisfy several of them. In an example, suppose that there are 10,000rules and that a given packet's headers may satisfy as many as ten ofthem. Communicating the results of the header-rules determination wouldtake 14 bits/rule×10 rules=140 bits.

One aspect of the present invention and its example implementationenables a more compactly listing of the header rules 452 that a givenpacket may satisfy. Specifically, header rules 452 may be dividedbetween “focused” and “promiscuous.” A “promiscuous” header rule may beunderstood (in the context of the present example implementation) torefer to those header rules that are satisfied by a packet but such thatthat packet also satisfies other header rules, including otherpromiscuous header rules. A “focused” header rule, on the other hand,may be understood (again in the context of the present exampleimplementation) to refer to those header rules that are satisfied onlyby a packet that satisfies no other focused packet rule, but such thatthat packet may additionally satisfy one or more promiscuous headerrules.

In order to list the rules a header may satisfy, the exampleimplementation may employ a “Header-ID” that 1) explicitly identifiesthe focused header rule that the header satisfies; and 2) encodes thecombination of promiscuous rules it satisfies. FIG. 18 depicts oneformat that may be used for this purpose. Note that the format depictedin FIG. 18 requires only 32 bits, but any other configuration may beused.

Referencing FIG. 18, the Header-Number identifies the focused headerrule that the header associated with the subject satisfies. Moreprecisely, its Header-Offset portion is the first number in a range ofnumbers that identify action rules 450 of which that focused header rule452 is a component. The numbers that may used to identify action rulesmay be such that they reflect the composite, header/content nature ofthe relevant action rule 450; the most-significant bits identify theheader rule 450, and the least-significant bits specify a choice amongthe content rules 454 that may be mated with that header rule to make upan action rule 452. But note that different header rules 452 may matewith different numbers of content rules 454, so the ranges of actionrules 450 that may require the same header rule may have differentlengths. Thus, the Header-Number's Header-Mask field conveys how manymost-significant bits represent identify the header rule 452 and,consequently, how many least-significant bits identify which of thepossible mating content rules 454 are intended. Note that FIG. 18 showsthe valid bits in the Header-Offset for each possible Header-Mask value.

Since a packet's headers can satisfy only one focused header rule 452,the Header-ID may not need to specify any other focused header rule 452.But note also that packet's headers may satisfy several promiscuousheader rules, and so the Header-ID must specify them. In practice,however, the total number of promiscuous rules may be relatively small.Moreover, only a very small fraction of the theoretical number ofcombinations of those rules may actually occur. Implementations of thepresent invention may take advantage of this fact by using theHeader-ID's Overlap-Number field to identify which of a number ofpreconfigured sets of promiscuous header rules 452 also match thecurrent packet.

Specifically, as shown in FIG. 19 and continuing to describe the exampleimplementation, the contents of the Overlap-Number field may be used toaddress a 256×32-bit bitmap that may be integrated with a correlationmodule. The purpose of this correlation module may be to determine whichaction rules 450 the packet satisfies. The values in this field may beused by the correlation module to determine if patterns detected in thescan are relevant to this packet. Perhaps only patterns associated withHeader-Numbers specified in this field are considered when correlatingpatterns to the rule set.

Referencing FIG. 18, in the example implementation, the M bit in theHEADER-ID may be set to indicate a rule match solely based on the headerfields. In this manner, the example pattern-detection circuitry mayinsert a header alert in the results message when the M bit is set.

The PT field may, in the example implementation, indicate the packettype of the current packet. This field may be derived from the protocoltype field in the IP header. The following table describes the encodingfor the PT field.

PT Packet Type 00 Other (i.e. not TCP, UDP or ICMP) 01 TCP 10 UDP 11ICMP

The header match table above may be used, in the example implementation,to search for header matches. The header match table may use theHeader-ID from the packet header to determine the matching HeaderNumbers. Each rule event and position event generated from the eventtranslation table is compared to the Header-Numbers to filter out eventsthat may be irrelevant based on the header rules matching performed.

In the example implementation, up to 33 Header-Numbers may be specifiedwith the Header-ID. The Header-ID may contain a Header-Number andan—Overlap-Number, as shown in FIG. 18. One Header-Number may beobtained directly from the Header-ID and loaded into Header-NumberRegister0. The other 32 Header-Numbers may be selected via a 256 entrybitmap with 32 bits in each entry. The Overlap-Number may then be usedas an offset into the bitmap memory to retrieve a bitmap entry. Each bitin the bitmap entry is associated with a Header-Number Register. Whenthe bit is set, its associated Header-Number Register may be judged tobe valid and may thus be used when checking rule events. If the bit iscleared, its associated Header-Number Register is not used duringcompares.

In the example implementation, note that Header-Number Registers 1through 32 may be configured at the time the rule set is updated.

In the example implementation, the Header-Number may consist of a 14 bitHeader-Offset field and a 4 bit Header-Mask field. The Header-Maskindicates the number of valid bits in the Header-Offset field. Thecomparison operation is performed by comparing the valid bits in theHeader-Offset to the equivalent bits in the RULE_NUMBER in the ruleevent. If all of the valid bits are equal, than the event ID matches theHeader-Number. A rule event is passed on as a matching rule event if theRULE_NUMBER matches any of the valid Header-Numbers.

To illustrate the use of the Header-ID, in the present exampleimplementation, suppose a value of 0x1541_(—)0002 is used for theHeader-ID and the bitmap entry for offset 0x02 is 0x0000_(—)0001.Header-Number Register 1 contains a header offset of 0x2388 and aHeader-Mask of 0x3. The Header-ID will produce a Header Offset of 0x0550and a Header Mask of 0x4 which is loaded into Header-Number Register 0.The overlap number from the Header-ID is 0x0002. The overlap number isused as an offset into the bitmap to read entry at bitmap offset 0x02.The table entry at offset 2 has only one bit set. With bit zero set,Header-Number Register 1 is enabled. All other Header-Number Registersare disabled. When a match event arrives with an event number of 0x0333,it is compared to Header-Number Register0 by masking out the invalidbits and comparing only the valid bits. The event number is changed to0x0330 by the Header-Mask of Header-Number Register0. A comparison ofbits [14:4] of the event number and the Header-Offset results in nomatch. The same sequence is performed for Header-Number Register1 whichalso results in no match. A match event arriving with an event number of0x0553 will match since masking it with the Header-Mask will produce0x0550 which is equal to the Header-Offset.

This scheme, as embodied in the example implementation and in variationsthat will be appreciated, is based on the fact that, in practice, mostheader rules do not overlap. When there are overlaps, the number ofoverlapping header rules may be relatively small. In the present exampleimplementation, 32 overlapping header rules are supported so that thecomplete rule base can only have 32 overlapping header rules. But in thepresent example implementation, if more overlapping Header-Numbers areneeded, the Header-Numbers can also be selected based on protocol type.In this case, the Header-Number registers 1 through 32 will consist of 4Header-Numbers. Thus, each Header-Number register is still enabled bythe bitmap but the packet type may be used to select one of the fourHeader-Numbers in each register. Note, however, that Header-NumberRegister 0 is not affected by these variations.

The teachings of the present invention may include processing of dataflow 444 using replay. Note that many protocols, (for example, in TCP),messages may be sent in multiple packets. The message may be reassembledat the destination from the individual received packets. Thus, whenscanning packets in transit, the data flow to be examined may not bepresent in a single packet. In addition, the packets may not arriveconsecutively, and the packets from a single message may be interspersedamongst packets for other messages.

In order to properly detect patterns in messages that are conveyed inthis type of protocol (such as TCP messages), the data in the packetsmust be reassembled much in the same fashion as the destination systemreassembles the packets into messages. This reassembly must also betransparent to the TCP protocol to avoid disrupting the communicationschannel. Thus, the present invention provides methods, illustrated inthe present example implementation, for achieving these goals bybuffering message data and executing patterns searches within thoseassembled messages (as described in the foregoing) without disruptingthe TCP protocol. The methods that may be applied in the present exampleimplementation include techniques for buffering forwarded data and forintelligently releasing buffers.

In the present example implementation, upon scanning a packet, thepattern-detection circuitry, may request a replay of the current packetwith the next packet of the TCP flow (other protocols may supportsimilar invocations). This operation is implemented in a recursivefashion; that is, after the replaying of 2 packets, thepattern-detection circuitry may then indicate that it requires anotherreplay of the original 2 packets plus a third.

In the example implementation, the pattern-detection circuitry may issuereplays when it encounters a partial rule match (that is, when asignature straddles more than one packet), and/or when it needs toperform some protocol preprocessing that requires inspecting stringsthat span more than 1 packet. In the present example, the BIF willalways forward the packet when a replay is requested and store a copy inthe TQUEUE.

FIG. 20 provides an example, in the context of the exampleimplementation, of a two-packet replay. In this example, the followingsteps are executed:

-   -   1) The BIF strips the L2 header and appends a BIF_(—)2_CSF        Header on Packet “A” and sends to the pattern-detection        circuitry. The “C” bit is set, enabling the replay function in        the pattern-detection circuitry if the pattern-detection        circuitry detects conditions that warrant a replay. The length        of the packet, excluding the L3 (IP) and L4 (TCP) headers is “n”        bytes.    -   2) The pattern-detection circuitry generates a result in the        CSF_RSLT message back to the BIF. In it, the returned offset        (START_OFFSET) is set to 0x0. This indicates that the BIF should        replay this packet with the next packet of the TCP flow (which        is indicated by the same FLOW ID). The BIF will proceed to store        a copy of packet “A” and forward the packet (RSLT=0x05). Note        that the packet “A” will also be forwarded to its next        destination based on the DEST_OP_TABLE.    -   3) The BIF receives packet “B”, the next packet of the flow,        strips its L2, L3, and L4 headers, and appends the payload to        packet “A”, which is in its original form. Both packets are sent        as one CSF_PKT with a payload length of n+m. The “C” bit is once        again set.    -   4) The pattern-detection circuitry scans the combined packet and        returns a result. The START_OFFSET is set to n+m. Since the        offset has advanced to the end of the combined packet, these        packets do not need replaying anymore.    -   5) The BIF then sends packet “C” in a similar fashion to how        packet “A” was sent in step 1). The process may then repeat.

Note that the result in step 4) could have come back with a“START_OFFSET=0x0”. In this case, the next replay would have been aCSF_PKT composed of the packet “A” (with its original L3 and L4 header),packet “B” (with its L3 and L4 headers stripped), and packet “C” (withits L3 and L4 headers stripped).

In one variation of this example, it may also be possible for thepattern-detection circuitry to return a START_OFFSET that points to themiddle of the payload of either the original sending of a packet (suchas in steps 1) or 5), above), and/or to the middle of one of the packetsthat were replayed (such as step 3), above). In such cases, the BIF willtake the original L3 and L4 headers of the packet to be replayed andappend the payload portion, starting at “START_OFFSET”, and then thenext packet will be appended as before. This combined packet will thenbe replayed to the pattern-detection circuitry.

The teachings of the present invention may encompass the inspection ofdata flows 444 to detect anomalous and/or malicious content within thoseflows. The foregoing disclosure outlines the operations embodied by theinvention in these operations, and paragraphs 89-219 outline one or moreexample implementations of the elements that may compose and/or may beassociated with content search logic 312.

The following paragraphs provide descriptions of example implementationsof the invention related to detecting anomalous and/or malicious contentusing one or more SOM's trained such to detect the presence of suchcontent and/or trained to detect the absence of content expected to bepresent in the context of the subject data flow 444.

Note, however, that as with the foregoing example implementation, thefollowing example may be employed in a wide range of implementations,configurations, embodiments, and the like.

FIG. 21 depicts the simplified communications processing system to whichthe following example implementation may be applied. Note, however, thatthis simplification is presented for pedagogical purposes and should notbe seen to limit and/or otherwise circumscribe descriptions and/orfigures presented herein or elsewhere.

The simplified example system of FIG. 21 receives communications in formof packets 404 on which it performs various operations. In an example,the system may treat the incoming traffic as divided into different“flows” characterized by respective features such as the node thatreceives the flow, the application (HTTP, SMTP, etc.), the payload(text, JPEG, etc.), other features, or combinations of those features,and among the system's functions may be to de-multiplex the incomingtraffic into such flows. As described hereinabove, data flow engine 308may, in this example, represent the consolidated functionality that mayperform this and/or other functions separate from the protectionfunctions discussed below. Note that, with the exception of forwardingtraffic, which may depend on such protection operations, the data flowengine 308 operations are not of interest in the following descriptions.

One point of interest regarding these functions, however, is that theflows may include content that may be considered “dangerous” and/orwhich may otherwise need to be specially monitored according torequirements of a particular instance of the following exampleimplementation. In practice it may often happen that such content ischaracterized by some signature that distinguishes it from “benign”content. Thus, one purpose of the functionality that may be embedded incontent search logic 312 is to search the incoming flows for suchsignatures. When such signature patterns are encountered, functionalityassociated with content search logic 312 may cause and/or signal someappropriate action, such as, for example and without limitation,preventing the offending flow from being forwarded.

Additionally or alternatively, there may be malicious flows for whichsignatures are not yet known. To detect such flows, the simplifiedexample system of FIG. 21, may additionally include a behavior-analysisengine 2104. In implementations, behavior-analysis engine 2104 mayattempt to find flows that differ (in some fashion) from those normallyencountered (presumably benign) flows. The behavior-analysis engine 2104may be or be associated with the machine learning logic 314 and/or themachine learning acceleration hardware 318.

In the example implementation that follows, behavior-analysis engine2104 may comprise a neural network for finding unusual flows. The neuralnetwork may be an instance of any and all neural networks for findingunusual flows, pattern matching, and so forth. Such neural networks aredescribed herein and elsewhere.

Some embodiments may use appropriately programmed general-purposedigital computers to implement the neural network (and/or otherstructures associated with and/or which may be embedded withinbehavior-analysis engine 2104). But some applications will requirereal-time filtering of high-speed data flows. In the context of thepresent invention, and in the following example implementation,“Real-time” filtering may be understood to mean that the netfunctionality of the apparatus depicted in FIG. 1 executes itsfunctionality such that any delay such a system may impose uponnon-anomalous flows would be maximally transparent (that is, at leastsmall enough) to permit proper (that is, normal) operation of flows inthe associated apparatus, but which also enable the system associatedwith the apparatus in FIG. 1 to interrupt detected anomalies before theycan do damage.

For many of these applications where “real-time” functionality may berequired (and/or for any other reason), dedicated hardware may be usedto execute some or all of these functions, where in the context of thepresent invention, and in the following example implementation, suchhardware may be used adjunctively and/or exclusively to appropriatelyprogrammed general-purpose digital computers.

FIG. 22 depicts in simplified form one possible hardware arrangement ofthe behavior-analysis engine 2104. (Note, however, that the functionsdepicted in FIG. 22 provide a pedagogical illustration of the followingexample implementation, and shall not be construed to limit, amend,and/or otherwise circumscribe previous depictions and/or descriptionsconveyed in the foregoing paragraphs and figures.) It will beappreciated that the behavior-analysis engine 2104 may a complete,partial, or alternate embodiment of the data flow processor 310 and/orany other element of the flow processing facility 102 as describedherein and elsewhere. A packet parser 2206 may divide the input(“traffic in 2204”; which, in embodiments, may be “the packets 402”)into “chunks” from which the system may extract respective featurevectors. A typical “chunk” may be an Internet Protocol (“IP”) datagramor other link- or other-level protocol data unit. As FIG. 22 suggests,packet parser 2206 may divide those “chunks” into header and payloadportions, from which header analyzer 2208 and content analyzer 2210 mayextract the features in different ways, as outlined in the followingdescription.

Header analyzer 2208 may extract features that may include, withoutlimitation, the various fields within the IP header and/or with anencapsulated transport-layer header. In addition, header analyzer 2208may also derive other features from statistics taken over multiple“chunks”. Examples of such other features may include, withoutlimitation, connection time, and/or requests per unit time, and/oraverage request and response sizes, and/or number of connection per unittime, and/or the number of connections to the same destination per Nconnections, and/or the multicast-to-unicast and unicast-to-multicasttraffic distributions.

Certain implementations may require that such “multiple-chunk”quantities be processed on a per-flow basis, but, as mentioned inforegoing paragraphs (see paragraphs 213 and 214 above), packets 402that carry data for a given flow may arrive out of order and/or may beinterspersed with packets that carry data associated with other flows.The feature-extraction operations detailed (in simplified fashion) inFIG. 22 may, therefore, need to reorder and/or reassemble those packets.Header analyzer 2208 and Content Analyzer 2210 may, in anyimplementation, provided Header RAM 2212 and Payload RAM 2214 (where RAMmay be understood to also include any read/write capable device) withinwhich data may be accumulated for the purpose of re-ordering and/orreassembling packets.

The example implementation may not only extract raw features fromtraffic in 2204, but may also normalize their values, as blocksnormalize 2218 and normalize 2220 indicate. (Note that thisfunctionality may be related to aspects of the invention that areconveyed by FIG. 4, and its descriptions, where blocks normalize 2218and normalize 2220 may produce and/or may modify and/or may beassociated with normalized data 428 and related functionality withindata flow engine 308).

The purpose of normalization may be to maintain inter-processsensitivity in the distance criteria (as described in the followingparagraphs) within processing that may be associated with the neuralnetworks 2224. Note, however, that some embodiments of the presentinvention may dispense with normalization. But note also that, inimplementations in which neural networks 2224 (and associated processes)may require normalization, the alignment functionality provided bynormalization may be accomplished in any number of ways, and/or mayemploy any number of techniques similar to normalization. Any of thesecases may be understood as being embodied in the present exampleimplementation by means of the blocks normalize 2218 and normalize 2220.

In the present example implementation, blocks normalize 2218 andnormalize 2220 may express the magnitudes of any and/or all of thecomponents extracted from “traffic in” 2004 and which may be deliveredby means of header analyzer 2208 and/or content analyzer 2210. Thesequantities may be expressed in terms of numbers of standard deviations,but many other representations may be employed. The result of theseprocesses may be a sequence of feature vectors applied to neuralnetworks 2224.

In the present example implementation, the functionality embedded withinneural networks 2224 may optionally be configured to operate on multipleflows concurrently, and in these cases, (as shown in FIG. 22) amultiplexer/finite-state-machine module (mux FSM 2222) may marshal theresultant feature vectors in manner and/or in a configurationappropriate to the neural networks 2224. For similar reasons,implementations may optionally integrate finite-state machine (resultfsm 2230) which may marshal the output from neural networks 2224 for useby subsequent circuitry and/or processes.

Referencing FIG. 23, in the present example implementation, each of whatmay be multiple neural networks 2302 (within neural networks 2224) maycontain J “neurons” 2304. Neurons 2304 may be treated as being spacedapart from each other in a virtual (typically but not necessarilytwo-dimensional) space, and, as discussed in the following paragraphs,operations related to functionality provided by this implementation maydepend on and/or may be influenced by the different “distances” betweenvarious neurons.

To illustrate one possible set of operations provided by the presentexample implementation, the features applied to the neural network 2224have been extracted from headers of an example flow (traffic in 2204).(Note that the present example implementation may utilize more than oneseparate neural networks 2224, such that one or more neural networks2224 may operate on header(s) from a flow, and one or more neuralnetworks 2224 may operate on the payload of that flow.) As FIG. 23indicates, the function of the jth neuron 2304 may be to compute aquantity d_(j). Each neuron may be characterized by a respective“weight” vector W_(j) [w₁, w₂, . . . w_(I)]^(T). The dimension of“weight” vector W_(j) is the same as the dimension of the input featurevectors, and the quantity d_(j) may, therefore, indicate how much weightvector W_(j) differs from the input feature vector F [f₁, f₂, . . .f_(I)]^(T). The measure used to assess that difference may be, forexample, the (scalar) Euclidean distance d_(j) between F and W_(j). Theresult of the neural network's computations for a given input featurevector is a vector D [d₁, d₂, . . . d_(J)]^(T) whose components composethose differences.

For high-speed communications, and where the present exampleimplementation may be implemented in hardware, it may be advantageousfor the circuitry that composes neural network 2224 to include multiplesimultaneously operable distance-computing circuits. In such cases, eachneuron 2304 may possess a separate, dedicated distance-computationcircuit. Implementations may provide separate such complete sets ofdistance-computation circuitry for each of a plurality of simultaneouslyoperating neural networks 2224. Moreover, some implementations mayenhance the foregoing with separately addressable weight memories foreach neuron 2304.

In many implementations, and in the present example, the neurons may beassigned their weight vectors during a “learning” phase. In such processor processes neural networks 2224 “learn” (in one or more processesduring which weights are adjusted) what may be considered “typical”(that is, non-anomalous) behaviors within data flows (as such behaviorsmay be characterized by the forgoing processes). The thus-determinedweights may then remained fixed (with exceptions to be described below)in a subsequent detection phase, in which neural network 2224 may beused to detect anomalous and therefore possibly malicious traffic.

FIG. 24 illustrates one example of this learning phase. This exampleoperation begins with initialization operation 2402. In initializationoperation 2402, each neuron 2304 may be randomly assigned a respectiveinitial weight vector and a respective neighborhood, which may consistof all neurons 2304 that may be ‘located’ within some limit distance.This neighborhood's size, which may shrink as learning progresses, mayinitially be large, and in some cases may at first encompass all of theneurons 2304 in neural network 2224. Initialization 2402 may alsoinclude adopting an initial gain value η, 0≦η≦1, whose purpose willbecome apparent in the following paragraphs.

As shown in FIG. 24, following initialization 2402, this exampleimplementation executes a loop in which it may operate on a sequence offeature vectors. In this learning phase, feature vectors may be obtainedfrom (one or more) data flow(s) of the type that may be monitored duringthe detection phase. The loop begins with process “receive featurevector 2404” which represents a process wherein the next such featurevector is input. In process “competition 2406”, the neurons 2304 may“compete” in the following fashion (in this example implementation):computational circuitry in the neural network 2224 identifies as the“winner” the neuron 2304 whose weight vector is closest to the currentfeature vector. (This example implementation uses Euclidean distance todetermine “closeness,” but note that many other measures may be usedinstead and/or in a supplemental fashion.) In some implementations, withappropriate normalization such as may be provided by Normalize 2218, the“winner” could be designated as the neuron 2304 associated with theweight vector W_(j) for which the scalar product W_(J)·F is thegreatest.) The process “competition 2406” is followed by process“cooperation 2408” wherein not only are the weights adjusted for winningneuron 2304, but those of its neighbors, as well.

Note that in the present example implementation of this process, thesame gain value η mentioned in the foregoing may be used. Note also thatthis value may be used for all neurons 2304 in the neighborhood, aswell, but that in some implementations, other approaches may beemployed. Some embodiments, for example and without limitation, maydispense with assigning neighborhoods explicitly but may instead usegains that vary as, say, a Gaussian function of distance from thewinning neuron.

In the present example implementation, if some appropriate criterion ismet, the routine may then adjust the gain function. This step isdepicted in FIG. 24 by process “adjust gain 2410.” Note that in thisexample embodiment, this adjustment may be adjusted using one or both ofthe following techniques: 1) reduce the gain value η; and/or 2) reducethe neighborhood size. (But note that many other techniques and/orcombinations that may include the foregoing may be possible in this andother implementations.) In this example, the criterion for decidingwhether to change the gain function may not be critical, and many suchcriteria may be integrated in this and/or in other embodiments. Thatdecision, for example, may simply derive from receiving a predeterminednumber of training vectors. Other criteria may be based on the averagedistance-vector value in some sliding time window, or the number ofvectors that have been processed so far. In any event, the routine inthe present example repeats the loop until some end-of-trainingcriterion and/or criteria (tested for in the process “end of training2412”) have been met. There may be any number of criteria (and/or groupsof one or more criterion) considered in the threshold functionassociated with process “end of training 2412”. In some implementations,for example, the criterion may be that every weight vector's distance tothe feature vector is less than some threshold, but there are manypossible threshold possibilities depending on the context requirementsof a particular implementation. In any event, when those criteria aremet, the learning phase is over.

FIG. 25 depicts an example implementation of the detection phase. In thedetection phase, neural net(s) 2302 thus trained may be used to detectanomalous flows. Following initialization (process initialize detection2502 in FIG. 5) the process “compute distance 2504” computes distancesbetween weight vectors and feature vectors as described in the in thelearning phase. (Note that, as in the foregoing, any number of methodsmay be used here.) In contrast with the learning phase, however, in thedetection phase, the weights remain fixed, and the computed distances(resulting from process “compute distance 2504”) are used not todetermine weight adjustments. Instead, the computed distances are usedto detect anomalies; if those distances meet some predeterminedcriteria, (which may compose one or more groups of criterion) the systemmay take some appropriate action, such as issuing an alert to othercircuitry or to supervisory personnel. As processes “test threshold2506” and “issue alert 2508” indicate, in this example implementation,an anomaly may be signaled if all distances exceed respectivethresholds. Note that in the example implementation, these distancesand/or the related thresholds may be some number of standard deviationsof the distances that may have been observed during training.

As stated in the foregoing, in the present example implementation, thepacket parser 2206 may parse the input stream (“traffic in 2204”) into aheader, or “connection” portion and a payload, or “content” portion, andheader analyzer 2208 and content analyzer 2210 may extract the featuresfrom those portions differently. To extract the payload features,content analyzer 2210, in this example implementation, may use amodified version of the N-Gram algorithm.

In an example, provided for the purposes of illustration and notlimitation, a window size of, say, two bytes is adopted, and the windowis advanced through the content payload chunk in steps of, say, onebyte. As it does so, the algorithm takes a histogram of varioussequences or groups thereof. In this example, each feature in thecontent-feature vector corresponds to some sequence or group ofsequences within the subject content, and the value (which may benormalized) of the corresponding histogram bin or bins composes thevalue for that feature.

Continuing with this example, using this illustrative algorithm, thesequence “Papaya” results in a unity value for each of the histogrambins representing the two-byte sequences “Pa,” “ap,” “pa,” “ay,” and“ya” and in a zero value for all other histogram bins. If one of thefeatures is the total of the values for the bins corresponding to “PA,”“Pa,” “pA,” and “pa,” then that feature's value (before normalization)will be two.

In the present example, if resultant distances fall outside a thresholdthe flow is considered to be anomalous. Note that such an algorithm mayalso be embedded in and/or executed by header analyzer 2208, but thatthe different algorithms may, in implementations, be applied in eachcase. In any event, a chunk (typically, a packet) may be declaredanomalous if either of the two neural networks detects an anomaly.

In many applications, it may be desirable to derive sets of weightvectors from training data drawn from narrow ranges of flow types but inthe detection phase to apply different flow types to neural networksthat may use the resultant different weight-vectors sets. For thispurpose, flow types may be classified in accordance with the functionalelement that receives the flow, including (but not limited to) theprocesses (and/or combinations of discrete processes) that deal withapplication (HTTP, SMTP, etc.), payload (text, JPEG, etc.), or othercharacteristics of data flow 444. In implementations, this may providemore accurate and customized modeling and, therefore, higher detectionrates and very low false-positive rates.

As was mentioned in the foregoing, in some implementations, high-speedcircuitry for implementing the present invention may include separatedistance-computation circuitry, as well as separately addressable weightmemories, for each neuron. In refinements, in order to “personalize” thecircuitry differently for different flow types, the distance-computationcircuitry for a given neuron may be provided memory not just for oneweight vector but for multiple weight vectors.

Such an example implementation is depicted in FIG. 26. In this figureneuron 2304 includes addressable memory 2602. This co-location mayenable the same computation circuitry to act in different time slots aspart of different concurrently operating neural networks that may bededicated to respective different flow types. Specifically, as differentflows arrive, corresponding different addresses may be applied to theweight memories from which the difference-computation circuits drawtheir inputs. In this manner, different SOM's may be implemented withinthe same circuitry. It will be appreciated that many variations on FIG.26 may be possible.

The reliability with which the neural networks detect anomalous behaviormay be directly affected by the accuracy of elements that are integratedwithin the training and learning phases. In a further extension of theteachings of the present invention, example implementations may providethe ability not only to learn directly from a customer's network butalso to implement incremental learning in a cost effective manner. Inexample implementations and without limitation, this may be accomplishedby using multiple SOMs in such a fashion as to enable the neuralnetworks to learn continually from the network while remaining indetection mode.

This example implementation embeds an apparatus that can gatherincremental knowledge from the network and apply it to the knowledge ofan existing SOM. (Note that this capability to add experientialknowledge to the SOM may also be applicable in real time intrusiondetection systems.) Since customer behaviors may change over time, it isalso necessary to integrate knowledge of these new and possible evolvingconditions to the existing SOM. If a detected anomalous behavior turnsout to be benign, for example, it may be desirable to include this newinformation into the existing SOM. Further, it may be desirable thatthat additional knowledge be added without losing and/or degradingand/or modifying existing knowledge. And yet, the existing knowledge mayhave been acquired during training that may have occurred months or evenyears ago. So, the network data that may have been used for that pasttraining may not be available. It is for these reasons (and there may bemany others) that it may be important to provide the capability to addincremental knowledge to existing SOMs.

FIG. 27 depicts an example implementation of a system that may providethe capabilities described in the foregoing. This example apparatus mayprovide this functionality when deployed in-line; that is, it may beconfigured to continually learn new behavior from the network, but mayalso, at pre-defined times, add incremental knowledge to the existingSOMs. In this example implementation, the SOM that is in the detectionmode may detect an anomaly, and may then form an “anomalous cluster.”This anomalous cluster represents new knowledge. The reference vectorsfrom this cluster (and not the actual network data) are then fed toanother SOM. The reference vectors of this incremental SOM along withthe reference vectors of the SOM with the acquired knowledge (note thatthis SOM may be the exact copy of the detection SOM) are then fed intothe new updated SOM. Thus, in this example embodiment, the SOM is nowtrained with the new set of reference vectors as features. Thenewly-trained SOM is now the SOM with the updated knowledge that is nowused for detection. Note that, as an additional optimization provided inthis example, since the new SOM is trained with the reference vectorswhose dimensionality has already been reduced, the incremental knowledgeacquisition process may be very fast.

In many applications of the teachings of the present invention,SOM-based neural networks may reduce false positives to arbitrarily lownumbers and may provide advanced logging capability. These enhancementsenable high detection rates for unknown attacks while keeping the falsepositive rates to a minimum. (In practice, in some implementations, thisrate has been observed to be as low as less than 1%.).%). At 10 Gb/srates, however, even this level of performance could overwhelm thenetwork administrator. Thus, the teachings provided by the presentinvention may be extended to include techniques that filter andcorrelate a large number of such events (>10K/s) and, as a result ofthese processes, may reduce these instances.

The present example describes a method and apparatus to reduce the falsepositive rates of the intrusion detection system using SOM neuralnetworks. Note that designing intrusion-detection systems involves atradeoff between the detection rates and the false-positive rates. Ifthe detection rates are kept low, then low false positives can belimited, but then new-attack detection may be missed. If the detectionrates are designed to be high so that more new attacks are detected, thefalse-positive rates can be unacceptably high.

In the present teaching and the example implementation that follows, aSOM-based neural network is described that may address these trade-offsby achieving low false-positive rates while keeping the detection ratesfor new attacks high. FIG. 28 depicts an example implementation of thistechnique. As shown, this example includes an “anomaly-class” table2802, which contains entries associated with respective neurons 2304.Each entry identifies neuron 2304 with which it is associated and mayinclude counter and threshold fields. In this example implementation, asthe system receives network data, it extracts feature vectors (asdescribed in the forgoing), (optionally) normalizes them, and appliesthem to the SOM lattice. If the SOM lattice finds anomalous behavior, itmay not, in this case, interrupt the processes that are tasked to takeaction (such as issuing an alert) in the event such anomalous behavioris detected. Instead, in this example, the event is recorded byincrementing the counter-field contents in the table entry associatedwith the winning neuron (neuron 8 in FIG. 28) in anomaly-class” table2802. A given attack will usually result in the same counter beingincremented repeatedly. Monitoring software may read the anomaly-class”table 2802 periodically and but may reset the contents of the counterfields. In this manner, it is possible thereby to keep track of therates at which various anomaly types occur, and, as a consequence, mayissue an alert only if a thereby-monitored rate exceeds a value that thecorresponding table entry's threshold field represents.

Referring again to FIG. 5, a data flow 444 may be handled by a flowprocessing facility 102, which may be incorporated into or associatedwith a unified threat management application 520, or which itself mayperform various unified threat management actions. The unified threatmanagement application 520 or action may encompass one or moreapplications or actions normally included in or associated with unifiedthreat management, including one or more of a firewall-relatedapplication or action, including updating a firewall application 514; anintrusion prevention system application 518 or action; an anti-virusapplication 522 or action; a URL filter application 524 or action; ananti-spam application 528 or action; another unified threat managementapplication 530 or action, an intrusion detection system application oraction, an anti-spyware application or action, an anti-phishingapplication or action, and so on. In certain embodiments, one or more ofthese unified threat management applications 520 may, consecutively orsimultaneously, process the data flow 444 or a representation thereof.This processing may be directed at providing a feature, function, orservice that is generally associated with unified threat management.Thus, the flow processing facility 102 may provide a unified threatmanagement feature, function, or service as it relates to a data flow444 by routing the data flow to a unified threat management application520.

In other embodiments, one or more actions related to unified threatmanagement may be embodied in the flow processing facility 102, asillustrated by examples to follow. In particular, a data flow 444 may beprocessed by the flow processing facility 102 to identify patterns inthe data flow 444, such as by using a set of artificial neurons, such asa neural network or the self-organizing maps described above. Patternsin the data flow 444 may be recognized that are relevant toidentification of a wide range of threats to the network, including thethreats managed by unified threat management applications 520. Thus, asdescribed above and in any of the embodiments described herein, the flowprocessing facility 102 may be configured to identify, and take actionwith respect to, data flows 444 that contain patterns that suggest theexistence of various types of threats. In embodiments the data flowprocessor 310 described herein may also include content search logic312, which may explicitly implement pattern recognition using regularexpressions (in one preferred embodiment the pattern recognition isembodied by an optimization of the Aho-Corasick algorithm). Thus,pattern recognition, in certain preferred embodiments, may consist ofapplying a set of artificial neurons such as a SOM or neural net,processing an output of the set of artificial neurons (e.g., thefingerprint 448), and performing a regular expression pattern match onpackets of the data flow 444, or any combination or sub-combination ofthe same.

In an embodiment, the flow processing facility 102 may be used to inassociation with a firewall application 514. The firewall application514 of this simplified example may be associated with TCP/IP and UDP/IPdata flows 444. When the flow processing facility 102 receives such dataflows 444, they may be associated with the firewall application 514. Theflow processing facility 102 may receive such data flows and test them(such as via pattern recognition using the SOM, or otherwise accordingto the various embodiments described herein) for malicious or malformedTCP/IP or UDP/IP headers (which may be encompassed by a TCP/IP or UDP/IPpacket 402), malicious or malformed TCP/IP or UDP/IP packets 402, or anyother TCP/IP or UDP/IP packet 402 or header of a questionable nature.The flow processing facility 102 or firewall application 514 may alsoconduct a test that checks a blacklist and white list to determinewhether a given packet should be summarily allowed or denied passagethrough the firewall application 514. The white list or blacklist mayspecify a destination IP address, a source IP address, a source port, adestination port, a time of day, a direction of transmission, or anyother aspect of a TCP/IP or UDP/IP data flow. Depending upon the resultsof the tests, the firewall application 514 or flow processing facility102 may allow or deny the packet 402 or the data flow 444. The firewallapplication 514 may employ stateful/state-sensitive packet inspection orstateless packet inspection. The application accelerator 504 may enablethe firewall application 514 or may expedite processing associated withthe firewall application 514. The RAM 510 or other memory facility maycontain an operative part or any part of the application 514 and the CPU508 may process an operative part of the application 514. It will beappreciated that the firewall application 514 in general embodiments mayprocess other types of data flows 444 and may not in any way be limitedto processing just the network and transport layers of the Internetprotocol stack.

In an embodiment, the flow processing facility 102 is used to enable orsupport an intrusion prevention system application 518 or to enable orsupport an intrusion prevention action. In embodiments the intrusionprevention action is accomplished by the flow processing facility 102,such as simultaneously with accomplishing other actions. In otherembodiments the flow processing facility may be embodied in theintrusion prevention system application 518. The intrusion preventionsystem application 518 or action of this simplified example may beassociated with preventing malicious network traffic. The flowprocessing facility may, among other things, test a data flow 444 forindications of an unauthorized attack on, access of, or use of anelement of the networked computing environment 100. In some cases, suchunauthorized actions are associated with a hacker, a virus, a Trojanhorse, a worm, spyware, phishing, and so forth using, for example,pattern recognition, such as using the SOM-based neural net or similarprocessing facility as described herein. The flow processing facility102 may test the data flow 444 for unauthorized actions, such as thosethat are driven by a virus or those that have characteristics of ahacker's attack on a network. The flow processing facility 102 may testfor a misuse or an anomaly embodied in the data flow 444. Generally, theflow processing facility 444 may provide access control for any of theelements of the networked computing environment 100. In embodiments theapplication accelerator 504 may enable this access control or mayexpedite processing associated with providing this access control. TheRAM 510 may contain an operative part or any part of an associatedintrusion prevention application 518 and the CPU 508 may process anoperative part of the application 518. In embodiments, the intrusionprevention system application 518 or the flow processing facility 102may differ from the firewall application 514 in that the intrusionprevention application 518 or flow processing facility 102 may provideaccess control based upon application-level content in the data flow444. It will be appreciated that the intrusion prevention systemapplication 518 or flow processing facility 102 in general embodimentsmay process any aspect of a data flow 444 in the manner describedthroughout this disclosure and is not in any way be limited toprocessing just the application-level content in the data flow 444.

In an embodiment, unified threat management is enabled by a flowprocessing facility 102 that is incorporated in or associated with ananti-virus application 518 or that enables an anti-virus action, such asin processing a data flow 444 to recognize patterns that are associatedwith viruses. The anti-virus action of this simplified example may beassociated with preventing a virus that is embodied in the data flow 444from transiting the flow processing facility 102. The anti-virus actionmay test a data flow 444 for the presence of a virus, such as bymatching a component of the data flow 444 to patterns associated withviruses, such as using a SOM-based neural net or other facility forrecognizing patterns as described herein. In embodiments the test mayfurther involve the use of a dictionary, look-up table, database,external data source, or similar facility containing viruses,information about viruses, names of viruses, signatures of viruses orother data indicative of whether a segment of code is a virus or part ofa virus. The application accelerator 504 may expedite the test, byembodying some or all of the logic required to compare the contents ofthe data flow 444 to the virus indicator. The RAM 510 may contain, forexample, a dictionary or look-up table (or other data facility) and anoperative part of the anti-virus application 522. The CPU 508 mayprocess the operative part of the application 518 in association with,for example, a dictionary or look-up table. Other embodiments of theanti-virus application 522 will be appreciated and all such embodimentsare intended to fall within the scope of the present invention.

In an embodiment, unified threat management is enabled by a flowprocessing facility 102 that is incorporated in or associated with a URLfilter application 524 or that accomplishes a URL filtering action. URLfiltering in this example may be associated with preventing access toparticular URLs, wherein a data flow 444 contains them or contains anattempt to access them. The flow processing facility 102 (which may be aSOM-based flow processing facility) may process a data flow 444 in orderto recognize patterns that suggest the presence of a URL or a request toaccess to a URL, such as one that is in a blacklist or that is otherwisesuspect. In one example, without limitation, the request is embodied asan HTTP GET. The blacklist may be a text file, an XML file, a relationaldatabase, or any other embodiment of a blacklist. If an offendingrequest is found, the URL filter application 524 may deny that request,such as by dropping the request from the data flow 444 and/or bytransmitting an “access denied” message to the facility that originatedthe request. In one example, without limitation, this message may beembodied as an HTML page. The application accelerator 504 may expeditethe test, by embodying some or all of the logic required to compare thecontents of the data flow 444 to the blacklist. The RAM 510 may containthe blacklist and an operative part of the URL filter application 524.The CPU may process the operative part of the application 518 inassociation with the blacklist. Other embodiments of URL filtering willbe appreciated and all such embodiments are intended to fall within thescope of the present invention.

In an embodiment, unified threat management is enabled by a flowprocessing facility 102 that is incorporated in or associated with ananti-spam application 524 or that accomplishes an anti-spam action. Theflow processing facility 102 of this example may be associated withpreventing e-mail spam that is embodied in a data flow 444 fromtransiting the flow processing facility 102. The flow processingfacility 102 may test a data flow 444 for the presence of spam, such asby recognizing one or more patterns that are associated with spam, suchas by using a SOM-based neural net or other pattern recognizing facilityas described herein. In embodiments, the flow processing facility 102 oranti-spam application 524 may further involve one or more of thefollowing: checking a DNS blacklist; checking a DNS white list;utilizing a content-based filter; statistical filtering; checksum-basedfiltering; authenticating a sender of an e-mail; checking the reputationof a sender of an e-mail; checking a ham password; a cost-based system;a heuristic filter; a tar pit; a honeypot; a challenge/response systemor method; a Bayesian filter; and so forth. If the result of the test isaffirmative, then spam has been found. In response to this, theanti-spam application 528 or flow processing facility 102 may, withoutlimitation, drop the data flow 444; remove the spam from the data flow444, leaving the rest of the data flow 444 intact; alter the spam, suchas by inserting a message into the subject line of the spam e-mail, sothat the recipient can easily identify the spam as such; and so forth.The application accelerator 504 may expedite the test, by embodying someor all of the logic required to conduct it. The RAM 510 may contain anoperative part of the anti-spam application 528. The CPU may process theoperative part of the application 528. Other embodiments of theanti-spam application 528 will be appreciated and all such embodimentsare intended to fall within the scope of the present invention.

In an embodiment, unified threat management is provided by a flowprocessing facility 102 that is incorporated in, or associated withanother unified threat management application 530 or that accomplishesanother unified threat management action. This application or action maybe any application or action providing or associated with an aspect ofunified threat management. The application accelerator 504 may be usedin association with this application 530, such as by providing ahardware implementation of logic that expedites the execution of theapplication 530. The RAM 510 may hold data associated with theapplication 530, including an operative part of the application 530. TheCPU 508 may process the operative part of the application 530 and thedata that is associated with the application 530. The other application530 is intended to encompass any and all unified threat managementapplications 520 and any and all aspects of a unified threat managementapplication 520 that will be appreciated but that may not be describedin detail or mention in the present disclosure or in the documentsincluded herein by reference. All such applications 520 and aspects ofapplications 520 are intended to fall within the scope of the presentinvention as they used in or adapted for flow processing facility 102.

The flow processing facility 102 may facilitate content inspection asapplied in a unified threat management application at the network layer.In addition to detecting abnormalities in a network layer packet header,content inspection of a network layer packet payload may reveal problemsthat can be addressed by the UTM application. In an example, the contentsearch logic 312 of the flow processing facility 102 may be used toinspect the payload of a network layer packet to detect strings that maymatch a form of invalid application layer packet header. A network layerpacket with such a violation may be acted upon by the UTM application toprevent the packet from reaching the network, and any and all connectionor data flow 444 associated with the packet may be terminated ordropped.

The UTM application may be facilitated by the techniques, methods,features, and systems herein described for applying the flow processingfacility 102 to content inspection. In addition to packet-header-basedbehavioral analysis and matching by the flow processing facility 102,content inspection (including, without limitation, packet-payload-basedbehavioral analysis and matching) may be applied to detect threatswithin payloads, threats affecting protocols, intrusions passing throughports, and attacks on system resources. The flow processing facility 102can be configured in a network to inspect content such that threatswithin payloads that can be detected by content matching can beprevented. Threats that compromise the integrity of one or more networkprotocols may be detected by the flow processing facility 102 throughcontent matching of packets associated with the protocol. The networkprocessor module 210 elements and application processor module 212resources may be applied to network traffic to detect protocolcompromising packet payloads as the packets flow through the flowprocessing facility 102 (substantially in real-time). Network trafficassociated with a port may be monitored by the flow processing facility102 with content inspection to ensure any payload destined for the port(or originating in the port) does not include threats, viruses, spam, orother intrusions detectable by applying content matching. Withappropriate security policy 414 defined in the flow processing facility102, system resources such as system files, user passwords, NMS, NEMS,and other key resources may be protected from attack by applying contentmatching to network traffic packet payloads. The resources of the flowprocessing facility 102 such as the network processor module 210elements (e.g. the data flow engine 308, the data flow processor 310,the content search logic 312, the machine learning logic 314, and/or themachine learning acceleration hardware 318) and the applicationprocessor module 212 elements (e.g. the application processing unit 502,and/or in the application accelerator 504) may be configured as hereindescribed to provide a unified threat management solution coveringpacket header and payload inspection.

All of the elements of the flow processing facility 102 and unifiedthreat management application 520 are depicted throughout the figureswith respect to logical boundaries between the elements. According tosoftware or hardware engineering practices, the modules that aredepicted may in fact be implemented as individual modules. However, themodules may also be implemented in a more monolithic fashion, withlogical boundaries not so clearly defined in the source code, objectcode, hardware logic, or hardware modules that implement the modules.All such implementations are within the scope of the present invention.

In general, the flow processing facility 102 and its unified threatmanagement applications 520 are in no way limited by the examples thatare provided herein. All possible embodiments of unified threatmanagement actions or applications 520 within or associated with theflow processing facility 102 are intended to fall within the scope ofthe present invention. Although some of the following examples ofunified threat management applications 520 and action may be simplifiedfor illustrative purposes, this simplification is for the purpose ofillustration and not limitation.

Referring generally to the invention described hereinabove withreference to all figures, it should be appreciated that architecture forflow processing has been described herein. The flow processing facility102 may embody, include, or encompass the architecture. The architecturemay comprise a chassis 218 with power supplies 220, fans 222, backplane224, and slots 214. Into each of the slots 214 a module 208, 210, 212may be inserted. From each of the slots 214 a module 208, 210, 212 maybe removed. Thus, the architecture may support the reconfiguration ofhardware through the rearrangement of modules within the chassis 218. Inembodiments, the architecture may comprise a rack-mount module, but nota chassis 218. In this case, the modules 208, 210, 212 may bepermanently installed in the rack-mount module and may not be so easilyremoved or inserted as they would be if installed in a chassis 218.Systems built according to the architecture may support redundancyand/or failover with respect to elements of the systems.

Beyond the physical reconfiguration of modules 208, 210, 212 and slots214, the systems that comply with the architecture may dynamicallyreconfigure themselves in response to a variety of factors. Some ofthese factors, without limitation, may include a power failure,equipment failure, device failure, element failure, software failure,network failure, a change in a network data flow, an overload condition,an under-load condition, the output of an optimization algorithm, theoutput of an algorithm, an output of a heuristic, a value in a look-uptable, an output of the machine learning logic 314, a configurationparameter received from a management server 228, an alert signal, anerror signal, an alarm signal, an informational signal, a signal, acharacteristic of a data flow, a user associated with a data flow, arule associated with a data flow, a security feature associated with adata flow, a specification associated with a data flow, a securitypolicy 414, an application identification 412, and the like.

The dynamic reconfiguration may encompass an adjustment to software,hardware, and/or the way the data flow 444 wends its way through theflow processing facility 102. One example of such a dynamicreconfiguration is described in detail hereinabove with reference toFIG. 6. However, other types of dynamic reconfiguration are possible. Inan example, and without limitation, the dynamic reconfiguration mayencompass, include, comprise, be associated with, or be in response toone or more items from the following list of items: the coupling ordecoupling of a server computing facility 108 to the flow processingfacility 102; the coupling or decoupling of a departmental computingfacility 110 to the flow processing facility 102; the coupling ordecoupling of the flow processing facility 102 to the internetwork 104,the coupling or decoupling of a network-connected computing facility 112to the internetwork 104; the coupling or decoupling of anetwork-connected computing facility 112 to the flow processing facility102 via a link-, network-, transport-, or application protocol; thefailure of a departmental computing facility 110; the failure of aserver computing facility 108; the failure of the internetwork 104; thefailure of a network-connected computing facility 112; the coupling ordecoupling of a management server 228 to a control processor module 208;the coupling or decoupling of a public network 202 to a networkprocessor module 210; the coupling or decoupling of a private network204 to a network processor module 220; the insertion or removal of amodule 208, 210, 212 with respect to a slot 214 in a chassis 218; thefailure of a module 208, 210, 212; a change in a data flow 444; theincrease of data in a data flow 444; the decrease of data in a data flow444; an anomaly in a data flow 444; a failure, start, or restart of thepublic network 202; a failure, start, or restart of the private network204; a failure of a slot 214; a failure of the passive backplane 224; anoverload of a module 208, 210, 212; an overload of a slot 214; anoverload of the backplane 224; a reduction in load on a module 208, 210,212; a reduction in load on a slot 214; a reduction in load on thebackplane 224; a failure of a power supply 220; a recovery of a powersupply 220; a replacement of a power supply 220; a failure of a fan 222;a recovery of a fan 222; a replacement of a fan 222; a failure, start,or restart of a management server 228; a failure, start, or restart of aphysical network interface 302; the coupling or decoupling of somethingto the physical network interface 302; a failure, start, or restart of aswitching fabric; an overload, under-load, or change in load on theswitching fabric 304; the association or disassociation of the switchingfabric with the backplane 224; a failure, start, or restart of a dataflow engine; an overload, under-load, or change in load on the data flowengine 308; a condition association with the data flow engine 308; afailure, start, or restart of a data flow processor 310; an overload,under-load, or change in load on the data flow processor 310; acapability of the data flow processor 310; an energy consumption of thedata flow processor 310; a measure of heat generated by the data flowprocessor 310; an overheat condition of the data flow processor 310; aprogramming or reprogramming of the data flow engine 308; a programmingor reprogramming of a data flow processor 310; a programming orreprogramming of a content search logic 312; a programming orreprogramming of a machine learning acceleration hardware 318; afailure, start, or restart of the machine learning acceleration hardware318; an association or disassociation between a machine learning logic314 and the machine learning acceleration hardware 318; a function ofthe machine learning logic 314; a function of the content search logic312; a function of the machine learning acceleration hardware 318; anoutput of the content search logic 312; an output of the machinelearning logic 314; an output of the machine learning accelerationhardware 318; a signal directed at or provided by the content searchlogic 312, the machine learning acceleration hardware 318, and/or themachine learning logic 314; a success or failure of the machine learninglogic 314; a success or failure of the content search logic 312; anaddition or removal of a data flow 444; a configuration or use of thephysical network interface 302; a communication of the data flow 444through the physical network interface 302; a division of a data flow444 into one or more packets 402; a provision of a packet 402 to thedata flow processor 310; a provision of the packet 402 to a cellgenerator; a provision of a packet 402 to the content search logic 312;an anomaly contained in one or more packets 402; a provision of a packetto the machine learning logic 314; an processing step provided by themachine learning logic 314; a processing step provided by the machinelearning acceleration hardware 318; a processing step provided by thecontent search logic 412; a fingerprint 448; a conversion of a packet402 to a data cell 408 by the cell generator 404; a conversion of apacket 402 to a fingerprint 448 by the machine learning logic 314; aconversion of a fingerprint 448 and/or a packet 402 into normalized data428 by the content search logic 312; the normalized data 428; thenormalized data type 424; the application group 442; the applicationidentifier 412; the identifier 430; the other identifier 440; thecustomer identifier 432; the service identifier 434; the service levelidentifier 438; the security policy 414; the cell router 410; an actionof the cell router 410; an overload, under-load, or change in load onthe cell router 410; a condition that is detected by or announced to thecell router 410; an action rule 250; a header rule 452; a content rule454; an activation or deactivation of the action rule 250; an activationor deactivation of the header rule 452; an activation or deactivation ofa content rule 454; an alert 442; a transmission or reception of analert 442; a transmission of a data cell 408 to a done logical block420; a transmission of a data cell 408 to a packet generator 418; ageneration of one or more packets from one or more data cells 408 by thepacket generator 418; a failure, start, or restart of the packetgenerator 418; a data cell 408 or other information transmitted to orreceive from an application processor module 212; a data flowconstructed from one or more packets 402; a transmission of a data flow444 out of the data flow engine 308 via the physical network interface302; a failure, start, or restart of an application processing unit 502;a failure, start, restart, installation, un-installation, activation,deactivation, run-time profile, measured resource utilization, predictedrun-time profile, predicted resource utilization, estimated run-timeprofile, or estimated resource utilization of an application 512; afailure, start, restart, installation, un-installation, activation,deactivation, run-time profile, measured resource utilization, predictedrun-time profile, predicted resource utilization, estimated run-timeprofile, or estimated resource utilization of a unified threatmanagement application 520; a failure, start, restart, installation,un-installation, activation, deactivation, run-time profile, measuredresource utilization, predicted run-time profile, predicted resourceutilization, estimated run-time profile, or estimated resourceutilization of a firewall application 514; a failure, start, restart,installation, un-installation, activation, deactivation, run-timeprofile, measured resource utilization, predicted run-time profile,predicted resource utilization, estimated run-time profile, or estimatedresource utilization of an intrusion protection system application 518;a failure, start, restart, installation, un-installation, activation,deactivation, run-time profile, measured resource utilization, predictedrun-time profile, predicted resource utilization, estimated run-timeprofile, or estimated resource utilization of an anti-virus application552; a failure, start, restart, installation, un-installation,activation, deactivation, run-time profile, measured resourceutilization, predicted run-time profile, predicted resource utilization,estimated run-time profile, or estimated resource utilization of a URLfilter application 524; a failure, start, restart, installation,un-installation, activation, deactivation, run-time profile, measuredresource utilization, predicted run-time profile, predicted resourceutilization, estimated run-time profile, or estimated resourceutilization of an anti-spam application 528; a failure, start, restart,installation, un-installation, activation, deactivation, run-timeprofile, measured resource utilization, predicted run-time profile,predicted resource utilization, estimated run-time profile, or estimatedresource utilization of an other unified threat management application530; a failure, start, restart, installation, un-installation,activation, deactivation, run-time profile, measured resourceutilization, predicted run-time profile, predicted resource utilization,estimated run-time profile, or estimated resource utilization of another application 532; a usage of RAM 510; a failure of RAM 510; a usageof a CPU 508; a failure of a CPU 508; an energy consumption of a CPU508; a temperature of a CPU 508; a usage of an application accelerator504; a failure of an application accelerator 504; an energy consumptionof an application accelerator 504; a temperature of an applicationaccelerator 504; an availability of RAM 510, a CPU 508, and/or of anapplication accelerator 504; an association of an application 512 withan application processing unit 502; an association of an applicationprocessing unit 502 with an application processor module 212; apredicted failure of an application processor module 212; a predictedfailure of an application processing unit 502; a predicted failure of anapplication 512, 514, 518, 520, 522, 524, 528, 530, 532; a predictedfailure of RAM 510; a predicted failure of a CPU 508; a predictedfailure of an application accelerator 504; a predicted failure of aswitching fabric 304; a predicted, anticipated, scheduled, unscheduled,unpredicted, foreseeable, unforeseeable, or unanticipated failure,success, change in load, change in availability, change in capability,change in nature, change in character, or change in performance of anyelement of the networked computing environment 100 or of the flowprocessing facility 102 or any of its elements, which are describedhereinabove with references to FIGS. 1 through 6; and the like.

In embodiments, the methods and systems disclosed herein may provide aflow processing facility for processing a data flow, and configuring theflow processing facility to recognize patterns in the data flow based atleast in part on learning (e.g., artificial neurons, an SOM-based neuralnet, and the like).

In embodiments, the data flow processor 310 may incorporate unifiedthreat management functionalities that are relevant to identifyingthreats of disparate types, including threats relevant to intrusiondetection, intrusion protection, anti-virus protection, anti-spywareprotection, and anti-spam protection, as well as other types of threats,such as related to phishing or unauthorized use of computer networkresources. In other embodiments, the data flow processor 310 may beincorporated within a unified threat management application such thatthe data flow processor 310 functionality is one of a plurality offunctionalities provided by the unified threat management application.In other embodiments, the data flow processor 310 may be independentfrom, but associated with, a unified threat management application suchthat the identification of disparate threat types described above hereinis provided by the data flow processor 310 in conjunction with anindependent unified threat management application, or the like.

A flow processing facility 102 that is implemented according to anarchitecture of the present invention may be capable of numerousconfigurations and reconfigurations, which may be manually applied orautomatically applied. In all, the configurations and reconfigurationsmay be directed at providing unified threat management or any otherfeature associated with processing a data flow 444 in a networkedcomputing environment 100. The architecture of the flow processingfacility 102 may react appropriately to failures, anomalies,predictions, requirements, specifications, instructions, and any otherinputs, outputs, or statuses that may be associated with the hardware,software, logic, or data flows of the facility 102.

Referring now to FIG. 7, a logical representation 700 of a flowprocessing facility 102 includes a data flow 444, a plurality of machinelearning logic 314, a plurality of applications 512, a data flow router702, and the flow processing facility 102. The data flow router 702 maybe a high-level, logical representation of features, functions, orelements of the flow processing facility 102 that are describedhereinabove with references to FIGS. 1 through 6. These features,functions, or elements may relate to accepting the data flow 444 as aplurality of network data packets 402, converting data packets 402 intoan internal representation such as a plurality of data cells 408,routing the data cells 408 from one element to another so as to allowthe data cells 408 to be received and transmitted by a plurality ofapplication processor modules 212, routing the data cells 408 so thatthey are eventually converted back into a data flow 444 and transmittedout of the flow processing facility 102, and so forth. Generally, thedata flow router 702 may encompass any and all elements of the processand data flow 400 that do not explicitly appear here, in FIG. 7. Theprocess and data flow 400 is described hereinabove with reference toFIG. 4.

To be clear, the data flow router 702 is provided for pedagogicalpurposes, to abstract away details that are described hereinabove withreferences to the other figures, so that discussion of the presentinvention can proceed with a particular focus on the relationshipsbetween the flow processing facility 102, a plurality of machinelearning logic 314, and a plurality of applications 512. The abstractionthat is the data flow router 702 is not intended to limit, reduce,hinder, minimize, or otherwise provide a limiting context for any aspector element of the flow processing facility 102.

In an embodiment of a flow processing facility 102 that is implementedaccording to the present invention, a data flow 444 may be receivedand/or transmitted by a data flow router 702. The data flow may also bereceived and/or transmitted by one or more of a plurality of machinelearning logic 314. The machine learning logic 314 may be operativelycoupled and/or in communication with the data flow router 702. Thiscoupling and/or communication may encompass the transmission ofinformation relating to a desired or appropriate routing of the dataflow 444. This routing may trace, specify, suggest, encompass, include,or comprise one or more paths for the data flow 444. These paths maybegin with an input of the data flow into the flow processing facility102, continue to and from one or more of the applications 512, andconclude with an output of a data flow 444 from the processing facility102. The paths may include parallelism, such as a branch in a path thatresults in two paths that the flow processing facility 102 executes inparallel. It follows that the paths may include merges, where two pathsthat may be been executing in parallel are brought back together in someway. A path may also include a terminus, where processing of a data flow444 ends and, perhaps, the data flow 444 that reaches the terminus isdiscarded.

It will be appreciated that the output data flow 444 may be related to,associated with, but not necessarily identical to the input data flow444. The difference between the input data flow 444 and the output dataflow 444 may be a function of the applications 512 to which the flowprocessing facility 102 subjects the input data flow 444. Theseapplications 512, and the order in which the input data flow 444 issubject to them, may be a function of the path or paths that the dataflow 444 follows through the flow processing facility 102. Within theflow processing facility 102, any number of intermediate data flows 444may exist between an input data flow 444 and its corresponding outputdata flow 444. Depending upon the path or paths, these intermediate dataflows 444 may exist in a serial or parallel temporal relationship withrespect to one another. In some cases, a data flow 444 may be discardedby the flow processing facility 102, resulting in either no output dataflow 444 or in an output data flow 444 that does not carry acontribution from the discarded data flow 444. In embodiments,communication from the machine learning logic 314 to the data flowrouter 702 may be “direct” or “indirect.”

An aspect, then, of the present invention may encompass methods andsystems for determining a path. In the preferred embodiment, the machinelearning logic 314 are implemented as self-organizing maps. These mapsare described in detail hereinabove the reference to FIG. 4. Inembodiments, the applications 512 may be security-related, such as theuniversal threat management application 520 and related applications514, 518, 522, 524, 528, and 530. Thus, a plurality of self-organizingmaps may receive and process an incoming data flow 444. In response tothis processing, the machine learning logic 314 may communicateinformation to the data flow router 702. This information may instruct,suggest, or imply a data path for the data flow 444. The data path maybe a function of this information and of the applications 512 that areavailable to the flow processing facility 102. Recall (for example, byreferring back to the discussion of FIG. 6) that the number and types ofapplications 512 within the flow processing facility 102 may bedynamically and automatically adjusted by the switch 102, perhaps inresponse to a feature, aspect, or quality of the data flow 444. Thus,the applications that are in a data flow's 444 path may be created,configured, adjusted, prepared, instantiated, or embodied in response toa path, either in advance of the data flow 444 following the path or ona just-in-time basis.

Each of the self-organizing maps 314 may be associated with a particularapplication 512 or type of application. In an example, and withoutlimitation, a self-organizing map 314 may be associated with ananti-virus application 522; a self-organizing map 314 may be associatedwith a firewall application 514; a self-organizing map 314 may beassociated with an intrusion protection system application 518; aself-organizing map 314 may be associated with a URL filter application524; a self-organizing map 314 may be associated with an anti-spamapplication 528; a self-organizing map 314 may be associated with another universal threat management application 530; a self-organizing map314 may be associated with a universal threat management application520; a self-organizing map 314 may be associated with any otherapplication 532; and the like.

In embodiments, the data flow processor 310 may incorporate unifiedthreat management functionalities that are relevant to identifyingthreats of disparate types, including threats relevant to intrusiondetection, intrusion protection, anti-virus protection, anti-spywareprotection, and anti-spam protection, as well as other types of threats,such as related to phishing or unauthorized use of computer networkresources. In other embodiments, the data flow processor 310 may beincorporated within a unified threat management application such thatthe data flow processor 310 functionality is one of a plurality offunctionalities provided by the unified threat management application.In other embodiments, the data flow processor 310 may be independentfrom, but associated with, a unified threat management application 320such that the identification of disparate threat types described aboveherein is provided by the data flow processor 310 in conjunction with anindependent unified threat management application 320, or the like.

In embodiments, the methods and systems disclosed herein may provide aflow processing facility for processing a data flow, and configuring theflow processing facility to recognize patterns in the data flow based atleast in part on learning (e.g., artificial neurons, an SOM-based neuralnet, and the like). When the data flow 444 is received by the flowprocessing facility, it may be more or less simultaneously provided to aplurality of machine learning logic 314 and to the data flow router 702.In other words, the path of the data flow 444 may include a split whereone copy of the data flow proceeds to the machine learning logic 314 andanother copy proceeds to the data flow router 702. Any of the paths maytraverse a buffer or other mechanism that serves to delay, howeverperceptibly or imperceptibly, the data flow 444 along one path. This mayallow the data flow 444 along one path to be synchronized with a dataflow 444 along another path. In one example that relates to thediscussion in this paragraph, it may take an amount of time for themachine learning logic 314 to process the data flow 444. During thistime, the copy of the data flow en route to the data flow router 702 maybe delayed intentionally by a buffer in the path to the router 702. Thismay allow the machine learning logic 314 enough time to process theircopies of the data flow 444 and to communicate with the data flow router702.

The processing of the data flow by the machine learning logic 314 mayserve to classify the data flow 444. Depending upon the association of aparticular machine learning logic 314 to a particular application 512,the classification may relate to whether the application 512 may beplaced in the path of the data flow 444. In an example, and withoutlimitation, a machine learning logic 314 that is associated with auniversal threat management application 520 may determine that the dataflow 444 is anomalous or contains an anomaly that may be relevant to,associated with, or require further processing by a universal threatmanagement application 520. Since a plurality of machine learning logic314 may process the data flow 444, it is possible that a plurality ofclassifications will be generated for a single data flow 444. Thoseclassifications may relate to a plurality of applications 512, whichmay, depending upon the classification, be placed in the path of thedata flow 444. The information that is communication between the machinelearning logic 314 and the data flow router 702 may include theclassifications or information associated with the classifications.

The data flow router 702 may receive complete or partial informationfrom or associated with the machine learning logic 314. In addition toor instead of the information that has already been mentioned, the dataflow router 702 may receive one or more application identifiers or oneor more security policies 414. The data flow router 702 may also receivethe data flow, a partial data flow, or a representation thereof. Basedupon the information received from or in association with the machinelearning logic, the data flow router 702 may construct a complete orpartial path, which may be represented implicitly or explicitly. In anycase, the data flow router 702, from time to time, may receiveadditional information from the machine learning logic 314. Thisinformation may be a function of additional parts of the data flow 444that have arrived at the flow processing facility since the lastinformation received by the data flow router 702. Alternatively oradditionally, this information may relate to processing of anintermediate data flow that may be provided by the data flow router tothe machine learning logic 314. In any case, the additional informationmay result in the data flow router 702 changing and/or completing thepath that it had already determined.

In embodiments, the machine learning logic 314 may encompass aself-organizing map. However, it will be appreciated that many otherembodiments of the machine learning logic 314 are possible. In alternateembodiments, without limitation, the machine learning logic mayencompass one or more of the following machine-learning algorithms,techniques, and approaches: concept learning; general-to-specificordering; decision tree learning; artificial neural networks; hypothesisevaluation; Bayesian learning; computational learning theory;instance-based learning; genetic algorithms; learning sets of rules;analytical learning; combining inductive and analytical learning;reinforcement learning; semantic nets; description matching; generateand test; means-ends analysis; problem reduction; basic search; optimalsearch; trees; adversarial search; rules; rule chaining; cognitivemodeling; frames; inheritance; commonsense; numeric constraints;symbolic constraints; propagation; logic; resolution proof;backtracking; truth maintenance; planning; analyzing differences;explaining experience; correcting mistakes; recording cases; managingmultiple models; identification trees; hill climbing; perceptrons;approximation nets; simulated evolution; recognizing objects; linearimage combination; establishing point correspondence; describing images;computing edge distance; computing surface direction; expressinglanguage constraints; responding to questions and commands; heuristicsearch; knowledge representation; predicate logic; representingknowledge using rules; symbolic reasoning under uncertainty; statisticalreasoning; weak slot-and-filter structures; strong slot-and-filterstructures; knowledge representation summary; game playing; planning;understanding; natural language processing; parallel and distributedartificial intelligence; learning; connectionist models; expert systems;perception and action; and so on. The example embodiments of machinelearning logic 314 that are provided in this paragraph may be drawn fromPatrick Henry Winston, Artificial Intelligence, 3^(rd) edition,Addison-Wesley Publishing Company, 1993; Elaine Rich and Kevin Knight,Artificial Intelligence, McGraw-Hill, Inc., 1991; and Tom M. Mitchell,Machine Learning, WCB/McGraw-Hill, 1997, all of which are includedherein, in their entirety, by reference. Many other embodiments of themachine learning logic will be appreciated by those of ordinary skill inthe art, and all such embodiments are encompassed by the presentinvention.

Referring again to FIG. 4, it will be appreciated from the precedingparagraph that the machine learning acceleration hardware 318 may be fora particular embodiment of the machine learning logic 314. Inembodiments, the machine learning logic may be for a self-organizing mapand may relate to the assisting or accelerating computations related tothe artificial neurons of the map. However, in alternate embodiments,where the machine learning logic 314 may not be a self-organizing map,the machine learning acceleration hardware 318 may be adapted for thatembodiment. Those skilled in the art will appreciate the algorithmic orcomputational complexities of a particular embodiment of the machinelearning logic 314. Moreover, those skilled in the art will appreciatethose aspects of the embodiment that may be accelerated or assistedthrough a hardware implementation. The machine learning accelerationhardware 318 may encompass these aspects. In some cases, these aspectsmay be associated with a math co-processing function; a math function;math; a look-up table; a search algorithm; digital signal processing; anapproximation algorithm; an optimization algorithm; Newton's method; arandom number generator; a stack; a heap; a tree; a counter; a learningalgorithm; a neural network; Bayes theorem; an encoder; a decoder; arunning average; an average; a square root; a probability; a network; agraph; a representation of information; a process applied toinformation; a hypothesis; a test; an assertion; a rule; a script; aplan; a goal; a rule; a trend; and so forth. Many other aspects will beappreciated and all such aspects are within the scope of the presentinvention.

Participants to a network may be operatively coupled to the network.This coupling may facilitate communication among participants of thenetwork, allowing sharing and exchange of data, messages, and the like.A network security system, such as the flow processing facility 102, maymanage the data flows 444 through the physical connection as it providesnetwork security services. During this management, the flow processingfacility 102 may glean information that allows it to providevirtualization with respect to the data flows 444. This virtualizationmay allow logical arrangements of policies, networks, behavioralanalyses, applications, and so on to be applied to the management of thedata flows 444. A benefit of these logical arrangements is that they maybe tailored to the data flows; consistent with a wieldy, logical model(as opposed to an unwieldy, physical model); and so forth. A benefit ofvirtualization is that the logical arrangements may be appliedprogrammatically, automatically, and/or transparently with respect to asource and/or sink (i.e. a transmitting computing facility and/or areceiving computing facility) of the data flows 444. In an example andwithout limitation, virtualization may be provided with respect to adata flow 444 as a function of the source and/or destination IP addressof the data flow 444.

Applications of virtualization may relate to any and all aspects of flowprocessing, unified threat management, and so on. In an example andwithout limitation, two servers may each communicate with a databaseover the network. Were this network physically segmented, such as with anetwork security appliance physically residing between the servers andthe database, both servers may be subjected to one intrusion detectionand prevention policy. A virtualized network security system, on theother hand, may support a plurality of virtual networks connected to thedatabase, perhaps regardless of the physical arrangement of the network.Thus, each of the servers in this example may be connected to thedatabase through different virtual networks. The security policy on eachof the virtual networks may be different and, perhaps, a function of theserver's identity.

Referring to FIG. 30, a simplified schematic of an enterprise network3000, while the physical network connections 3002 may connect allparticipants to flow processing facility 102, the informationtransferred along the physical connections may be used to virtuallyseparate one or more participants from one or more other participants.For simplification, the following will describe examples ofvirtualization of internal participants of a network. However, the sameexamples could be applied to external participants such as clients,vendors, users, auditors, regulatory agencies, and others connectingover the internet. The same could also be applied to participantsconnected through VPN or wireless connections. This simplification in noway is intended to restrict or otherwise limit the scope of thevirtualization methods and systems here disclosed.

Again referring to FIG. 30, user1 3004, user2 3008, server 108, areexample participants of the network 3000 while engineering 3010, andsales 3012 are example participant types of the network 3000. As isshown, each of the user1 3004, user2 3008, server 108, engineering 3101,and sales 3012 have a physical network connection to flow processingfacility 102. The network connection to flow processing facility 102 maybe a single shared connection, or may be a plurality of individualand/or shared connections, or some combination thereof. While theexample network depicted in FIG. 30 is used to illustrate methods andsystems of network security virtualization, many other configurationsand uses of network security systems may be virtualized and all suchvirtualizations are within the scope of the present disclosure. Networkconfigurations suitable for enterprise, individual user, home user, homeoffice user, service provider, security provider, central office, remoteoffice, data provider, university, social club, public facility,library, town offices, state offices, federal offices, virtual privatenetwork, and any other network that may benefit from security may employvirtualization within a security deployment. Security deployments suchas unified threat management, intrusion detection, intrusion prevention,intrusion detection and prevention, internet firewall, URL filtering,anti-virus, anti-spam, anti-spyware, http scanning, applicationfirewall, xml firewall, vulnerability scanning, and any and all othernetworked security deployments may be virtualized and may include flowprocessing facility 102 as herein disclosed.

Embodied within flow processing facility 102 may be a virtualizationmodule 3014 that may uniquely identify data flows 444 from eachparticipant and logically route a data flow 444 from a participant to avirtual network 3018 associated with that participant. Security policies3020 for each virtual network 3018 may be applied to the data flows 444associated with the virtual network 3018. In an example, user1 3004 maybe associated with virtual network 3018′ that uses security policy 3020′while server 108 may be associated with virtual network 3018″ that usessecurity policy 3020″. In this example, which is provided for thepurpose of illustration and not limitation, security policy 3020′ mayimpose URL filtering restrictions for user1 3004 that may not exist insecurity policy 3020″ for server 108 such that user1 3004 may berestricted from accessing non-business related websites during businesshours.

Any and all aspects of flow processing facility 102 may be directed by asecurity policy 3020 to be applied to a data flow 444 of a virtualnetwork 3018 including, without limitation a content scanning functionfor providing an anti-virus feature; an anti-spam feature; ananti-spyware feature; a pop-up blocker; protection against maliciouscode; an anti-worm feature; an anti-phishing feature; or a protectionagainst an exploit.

Again referring to FIG. 30, security for network participants may bevirtually grouped such that all members of a group may share securitypolicy settings. When visualization is employed to eng 3010, each memberof the group eng 3010 will be associated with a virtual network 3018even though there may be no physical separation of network traffic fromparticipant group sales 3012. As network traffic associated with eng3010 participants is transferred through flow processing facility 102,the virtualization module 3014 may route eng 3010 data flows to virtualnetwork 3018′″ that employs security policy 3018′″. In this example,virtualization of the network security associated with flow processingfacility 102 may permit eng 3010 participants to access resources ofsales 3012 participants (such as a price list or customer list) whilepreventing sales 3012 participants from accessing eng 3010 resources(such as source code).

Connection among virtual networks 3018 and other resources such as theinternet 3022, wireless ports 3024, VoIP ports 3028, and VPN ports 3030may be accomplished by switching fabric 304 as described herein andelsewhere. In embodiments, switching fabric 304 may facilitate logicalconnection of any number of virtual networks 3018 with other resourcesas herein disclosed thereby enabling each network participant to havesecure access as defined by their associated security policy 3020 toshared network resources such as and without limitation a PC, cellphone, pager, laptop, PDA, networked sensor, set-top box, video gameconsole, TiVo, printer, VoIP device, handheld computer, smart phone,wireless e-mail device, Treo, Blackberry, media center, XBOX,PlayStation, GameCube, palmtop computer, tablet computer, barcodescanner, camera, and the like.

Virtualization of a networked security deployment may also be used toshare network security hardware resources such as a firewall amongotherwise separate networks. By associating each separate network with avirtual network 3018, each network administrator or owner may definesecurity policy for their network. The security policy defined may beapplied to network traffic associated with their virtual network 3018.Examples of network configurations that may be virtualized in this wayinclude without limitation remote branch offices, individual enterprisesleasing security from a security provider, and data storage serviceproviders.

Virtualization may be applied to aspects of a network securitydeployment such that each aspect may be provided policies and updatesseparately. A network security deployment may include a firewall,intrusion detection and prevention, URL filtering, and anti-virusaspects. In one embodiment, a plurality of virtual networks may beestablished such that each virtual network may be associated with one ormore security elements. As an example, a virtual network may beestablished to connect network resources to the internet and a firewallmay be configured between the virtual network and the internet.Additionally a virtual network may be established to also connect thenetwork resources to the internet with intrusion detection andprevention security configured between the internet and the virtualnetwork. Likewise virtual networks that provide URL filtering andanti-virus protection may be configured between the internet and thenetwork resources. Each security aspect could be managed separatelyfacilitating pushing policies and updated to the various aspectsseparately without impacting others.

In addition to virtualizing aspects of a network security deployment,virtualization may be applied across a plurality of flow processingfacilities 102. In a configuration in which the plurality of flowprocessing facilities 102 are connected substantially in parallel (e.g.for increasing performance), virtualization may be applied across theplurality of facilities 102 to facilitate applying common configuration,security policy 414, and the like. This may result in the plurality offlow processing facilities 102 appearing as a unified network securityentity rather than individual entities each requiring configuration,security policy 414, and the like. As an example, a networkconfiguration may include a plurality of flow processing facilities 102providing an interface between an enterprise network and the Internet.The plurality of flow processing facilities 102 may be configured withvirtualization as if they were one flow processing facility 102 bydirecting a common configuration (i.e. security policy 414) to each ofthe facilities 102.

Alternatively or additionally, individual modules (e.g. networkprocessing module 210, or application processing module 212) within aflow processing facility 102 may be virtualized into a single networksecurity entity. The modules may appear as one virtual network securityresource even though they may be physically connected to differentnetworks or network segments. In this way, common security policy,configuration, maintenance, and the like may be applied to the modulesthrough the virtualized embodiment. This virtualization of individualmodules may also be applied to individual modules in separate flowprocessing facilities 102. It may also be applied to separate flowprocessing facilities 102 that are not parallelized but instead areserving separate segments of a network (virtual or physical).Virtualized flow processing facilities 102 may be remotely located fromeach other through a public interconnection such as the Internet.

Virtualization of network security may also facilitate improvements innetwork security. Virtualization module 3014 may define a developmentvirtual network that mirrors a user virtual network such that allinternet traffic for the user virtual network also propagates to thedevelopment virtual network. Security policy of the development virtualnetwork may be updated with experimental intrusion prevention algorithmsand techniques that are being tested without causing intrusion orcritical false rejects on the user virtual network.

Virtualization of network security may also facilitate load balancing ofresources within a flow processing facility 102 by routing data flowassociated with one virtual network to one of a plurality of applicationprocessor modules 212 while routing data flow associated with anothervirtual network to another of the plurality of application processormodules 212. Alternatively, virtualization of network security mayfacilitate optimizing utilization of a flow processing facility 102 byrouting data flow from a plurality of virtual networks to oneapplication processor module 212. Routing may be provided by thevirtualization module 3014, the switching fabric 304, or a combinationof both.

The management server 228 may provide control, configuration, andmonitoring of the visualization module 3014 and/or the flow processingfacility 102 such that virtual networks 3018 may be defined andconfigured and security policies 3020 may be associated with the virtualnetworks 3018.

A flow processing facility 102 may be adapted to provide secureweb-to-network connectivity to protect against threats, intrusions, andthe like through the use of SSL encryption such as and withoutlimitation the encryption included with the Internet Explorer browser.Data flows 444 passing through the flow processing facility 102 from theinternet may be processed according to security policy 414 that includesSSL encryption to ensure threats are detected and preventive actions aretaken. By configuring the flow processing facility 102 to provideinterconnection of external resources to network resources, the networkresources may be protected.

In an embodiment, the flow processing facility 102 may be configured toseparate network resources from web based devices such that traffic fromeach web based device must pass through the flow processing facility 102to reach the network resources. Flow processing facility 102 may beconfigured to support a variety of typical web based activities throughan SSL connection such as shared files, email, instant messaging, andweb applications.

Security policy 414 for each web based client may be separately definedand employed by the flow processing facility 102 enforcing anappropriate security policy for each client. In an example and withoutlimitation, a web based client associated with a new employee may beonly permitted to access limited functionality and resources of thenetwork. In another example, an airport internet kiosk may be subjectedto a security policy that prevents access to confidential data on thenetwork. The flow processing facility 102 may prevent intrusion orthreats detected in SSL communication from a web client from affectingthe network.

Additionally a web based client that may be identified as transmittinginfected or malicious data flow may be quarantined such that furtherdata flow or new connection requests from the client will be dropped. Asan example and without limitation, a web client computer that isattempting to propagate a virus over an SSL connection to a network thatis detected by the flow processing facility 102, may have all furtherpackets 402 associated with the web client dropped or directed to asecurity port for further analysis. Quarantining web client devices mayalso facilitate security patch installation such that the infectedclient may remain quarantined from the network resources until the patchis deemed effective. In addition to the preventive actions hereindescribed when an intrusion attempt or threat is detected, the flowprocessing facility 102 may issue an alert 442 that may be separatelycommunicated to a management server 228.

The flow processing facility 102 may also provide SSL and VPN protocolintrusion detection and prevention. Even though a VPN connectionprovides a means of securely connecting a web client to network webinterface, such as a flow processing facility 102, the content andprotocol of the VPN tunneling application may be analyzed for threatsand intrusions. The VPN protocol may include IPsec with encryption, L2TPinside of IPsec, SSL with encryption, MPLS through BGP (layer 3 VPN),)and MPLS (layer 2 VPN). The flow processing facility 102 may beconfigured with security policy 414 such that web traffic associatedwith VPN tunneling applications can be analyzed for anomalies that mayindicate intrusions and threats. By using the resources of the flowprocessor facility 102 such as the data flow processor 310, the securitypolicy 414, and the application processor module 212, web networktraffic data flowing into the flow processing facility 102 may becompared and analyzed for anomalies in the protocols disclosed herein.

The flow processing facility 102 may also be configured to providesecurity for web infrastructure devices such as web servers. Securitypolicy 414 may be configured to facilitate detection of common webapplication threats such as buffer overflow, command injection, SQLinjection, malicious code intrusions, and the like that may eludesignature-based detection. Machine learning logic 314 may include selforganizing maps or neural network algorithms for learning webinfrastructure intrusions.

In an embodiment, the flow processing facility 102 may be embodied as aclient software application further facilitating secure connection of aweb based client running flow processing software embodying thefunctionality disclosed herein for the flow processing facility 102. Insuch an embodiment, the flow processing facility 102 may be used todetect and prevent spyware and malware on a client device. Additionally,a flow processing facility 102 software embodiment may performconversion of client communication to secure SSL protocol forcommunicating with an enterprise network. The flow processing facility102 software may securely encrypt all web network traffic files such asemail, attachments, cookies and passwords on the client therebyfacilitating preventing sensitive information on the client from beingviewed or stolen from the web client. Such an application may beparticularly beneficial in public use clients such as and withoutlimitation airport internet kiosk PCs.

In another embodiment, a flow processing facility 102 may be configuredto provide a secure VPN gateway for a network. The network may includeservers such as Intel or AMD based servers running Linux or anequivalent OS that has been adapted to integrate network security withthe flow processing facility 102. This may provide the benefit offacilitating scalable, fault-tolerant, network security usingindustry-standard dynamic routing protocols such as IGRP, EIGRP, BGP,OSPF, RIPv1 and RIPv2, and multicast protocols such as IGMP, PIM-DM, andPIM-SM, SRM, RMTP, MTP-2, RAMP, TMTP, LORAX, SCE, RMP, and NTE. Such aconfiguration may provide load sharing of resources within a flowprocessing facility 102 as well as across a plurality of flow processingfacilities 102. In an example and without limitation, one or more flowprocessing facilities 102 may be configured in this embodiment such thatwhen any one facility 102 or a module within a flow processing facility102 fails, the data flows 444 associated with the failure areimmediately routed to other facilities 102 or modules such that thenetwork does not become victim to a “ripple effect” or otherinterruption.

A flow processing facility 102 may be adapted to provide networksecurity to protect against internal threats such as worms, denial ofservice, email-borne malware, and the like. Data flows 444 passingthrough the flow processing facility 102 may be processed to ensure suchthreats are detected and preventive actions are taken. By configuringthe flow processing facility 102 to provide interconnection of internalnetwork resources, the resources that are interconnected by the flowprocessing facility 102 may be protected.

In an embodiment, the flow processing facility 102 may be configured toprovide network traffic separation between one or more segments of thenetwork such that each segment's network traffic must pass through theresources of the flow processing facility 102 to reach another segment.Flow processing facility 102 may be configured in bridge mode, switchmode, or router mode to provide effective segmentation. In such aconfiguration, at least some of the network resources on a segment sharesecurity policy as it may be employed in the flow processing facility102.

A security policy for each segment may be separately defined andemployed by the flow processing facility 102 enabling segments withcritical network resources to enforce a stricter security policy thansegments with less critical resources. The flow processing facility 102may prevent intrusion or threats detected in one segment from affectingother segments of the network. Segmentation may be physical with eachsegment connecting to a different port on the flow processing facility102, or it may be logical based on IP address or other network deviceproperty. As an example and without limitation, each segment may beconnected to a different network processor module 210 of the flowprocessing facility 102 and each network processor module 210 may beconfigured with different data flow 444 control parameters.

Additionally, a network client such as a server or user computer thatmay be identified as transmitting infected or malicious data flow may bequarantined through dynamic reconfiguration of a segment into logicalzones. As an example, a user computer that is attempting to propagate avirus over the network that is detected by the flow processing facility102, may have all further packets 402 associated with the user computerMAC address dropped or directed to a security port for further analysis.Quarantining and logically separating client devices may also facilitatesecurity and software patch installation such that the infected clientcomputer may remain quarantined from other network resources until thepatch is deemed effective. If a client device or segment traffic isdetermined to contain an intrusion or threat, flow processing facility102 may take preventive actions and/or may issue an alert 442.

The flow processing facility 102 may also provide protocol intrusiondetection and protection. In an example, network protocols such as CIFS,DCOM, MS RPC, MS SQL, and so on may be analyzed for anomalies that couldindicate an intrusion or threat. Flow processing facility 102 may alsoprovide protection of protocols such as Citrix ICA, CDE RPC, HTTP, SunRPC, and so on. By using the resources of the flow processor facility102 such as the data flow processor 310, the security policy 414, andthe application processor module 212, data flowing into the flowprocessing facility 102 may be compared and analyzed for anomalies inthe protocols disclosed here and any other network protocol that may berepresented by packets 402.

The flow processing facility 102 may also be configured to providesecurity for web infrastructure devices such as web servers. Securitypolicy 414 may be configured to facilitate detection of common webapplication threats such as buffer overflow, command injection, SQLinjection, malicious code intrusions, and so on that may eludesignature-based detection. Machine learning logic 314 may include selforganizing maps or neural network algorithms for web infrastructureintrusion detection.

An embodiment that may provide the benefit of reducing the complexity ofadministrative setup of rules while providing security of the networkfrom internal sources may combine intrusion detection learningcapabilities of a flow processing facility 102 with firewallfunctionality. Since network traffic from internal network resources maybe less risky than externally generated traffic, the machine learningfunctionality 314 (e.g. algorithms for analyzing network traffic foranomalies based on rate) may be used to determine what is abnormaltraffic through a flow processing facility or segment (virtual orphysical). The security policy 414 applied to the packet and data flow444 filtering capabilities of the firewall 514 may be automaticallyupdated based on the anomalous patterns learned by the machine learningfunctionality 314. This may result in the firewall being directed todrop packets associated with a flow determined to be bad without havingto establish complex rule sets for network security.

Alternatively, the network conditions determined to be bad may beprovided, such as through alert 442 and management sever 228, to anadministrator who may manually update the firewall to filter out thenewly detected intrusion or threat conditions. Such a method allows thenetwork administrator to assess the internally detected conditionsseparately from externally generated threats. This may be a workablesolution for some network configurations if the number of alerts 442remains manageable without critically compromising the integrity of thenetwork resources and data.

Content inspection may reveal details about a packet or flow of packetsthat cannot be determined by only examining the packet header. Contentinspection may allow a determination of the nature of the data beingcarried in the packet. Such determination may be accomplished bymatching the content of the payload to known information such as thatwhich can be detected by regular expression matching. Details such asthe source website of a packet and the type of data (e.g. audio, video,email, executable code) may allow the content to be classified andtherefore more easily inspected for security threats, intrusions,extrusion, and the like. In an example and without limitation, if thepayload of a packet or flow of packets is classified as a portion of anaudio file (such as an MP3 file) then other packets in the associatedflow may also be expected to be audio file content. If evidence providedby further payload inspections contradicts this expectation, then anetwork security threat (such as and without limitation a Trojan horse)may be present in what originally appeared to be an audio file.

By the nature of how packets in a packet switching network relate to thelayers of communication protocol in such a network (e.g. internet IPstack layers), packet payloads associated with one level may includeboth header and payload information for another level. Thereforefacilitating content inspection at one layer may require performinginspection of packet header and payload information for another layer.In an example and without limitation, facilitating content inspection atthe network layer may require performing inspection of packet header andpayload information for the application layer. In this example, to theextent that the protocols and data constructions associated with theapplication layer may be substantially more complex than thoseassociated with the network layer, an inspection platform, such as theflow processing facility 102, may be needed for efficient, effectivenetwork security.

Content inspection may also be performed using behavioral anomalydetection techniques. By evaluating packet payload content, time-historybehavioral metrics of the content may be developed. As behavioralmetrics of each new packet or flow of packets are developed and comparedto the time-history metrics, critical aberrations may be detected, whichmay indicate an intrusion or threat to the network security. Whiledeveloping broadly based behavioral metrics for use in contentinspection may facilitate network security, applying behavioral anomalydetection techniques that are based on the content associated with aprotocol layer may facilitate detection of additional threats orintrusions not detectable at a broad level. Elements of a flowprocessing facility 102 may facilitate developing and/or calculatingmetrics and detecting behavioral anomalies for the content of packets ina variety of protocol communication layers.

Content inspection may be performed by a computing facility connected toa network when the packets that comprise network traffic are directed tothe computing facility for content inspection. The computing facilitymay perform content inspection by applying content matching andbehavioral analysis algorithms implemented in software, firmware, orhardware. The computing facility may comprise a general purposeprocessor (e.g. a COTS processor herein described) that may executesoftware embodying methods for inspecting the payload of packetsaccessed by the processor. Alternatively, the computing facility maycomprise a special purpose processor providing flow processing resourcesto efficiently perform processing of network traffic packets.Programmable or special purpose hardware such an FPGA, programmablelogic device, ASIC, and so forth may be configured as a packetprocessing engine for executing these methods, in hardware, on networktraffic.

The flow processing facility 102 herein described may comprise one ormore of a general purpose processor, a special purpose processor, andprogrammable hardware, and the like and therefore may facilitate contentinspection. As herein described, the application processor module 212 ofthe flow processing facility 102 may comprise one or more applicationprocessing units 502 that, in an embodiment, encompass a COTS processor.Software encompassing the content inspection methods for performingcontent matching and/or behavioral anomaly detection may be embodied inthe applications 512 as herein described. Therefore, the applications512 of software containing the content inspection methods may beuploaded, stored, and/or built into the application processing unit 502.As the application processing units 502 may be a COTS processor, thecontent inspection software may be compiled into a native formatcompatible with the COTS processor prior to being uploaded. Themanagement server 228 may facilitate compiling and uploading the contentinspection software to the application processing unit 502 of the flowprocessing facility 102.

Content inspection software may make use of the application accelerator504 of the flow processing facility 102 such that network traffic beinginspected may maintain a satisfactory throughput rate. When the contentinspection software is compiled (for example and without limitation, bythe management server 228) for the flow processing facility 102, aspectsof the software may be directed toward the application accelerator 504.In embodiments, FPGA code may be generated for programming theapplication accelerator 504. The management server 228 may profile theexecution of the content sensing software in order to identify acritical section that is computationally intensive. This criticalsection may be dynamically programmed into the application accelerator504 FPGA to provide an accelerated execution of the critical section andmay result in improved network performance or improved packet payloadinspection.

The flow processing facility 102 may include a network processing module210 that may also play a critical role in content inspection. Networktraffic passing through flow processing facility 102 physical networkinterface 302 may pass into the data flow engine 308 where each packetmay be further processed by resources such as the data flow processor310 and cell router 410. Data flow processor 310 may process packets todetermine behavioral metrics of packet payload using the machinelearning logic 314 and machine learning acceleration hardware 318. Thedata flow processor 310 may be programmed to distinguish among thevarious protocol layers that may be present in a packet payload suchthat the payload associated with a packet associated with a protocollayer may be analyzed for behavior related to the protocol layer. Inthis way, the payload of a packet identified as a network layer protocolpacket (based on the packet header information) may be inspected by thedata flow processor 310 such that any transport layer packets orapplication layer packets found within the network layer packet payloadcan be distinguished for behavioral analysis. The data flow processor310 may be preconfigured to analyze the behavior of one or more protocollayer packets. Alternatively, the data flow processor 310 may beconfigured to perform content inspection of any one or any plurality ofdifferent protocol layer packets. The data flow processor 310configuration may be performed through the management server 228 asherein described. The data flow processor 310 may execute a program thatmay be installed through the management server 228. Such a program maybe a compiled output in a native format for the data flow processor 310.The program may be provided to the management server 228 for compilationor it may be precompiled by another network computing facility beforedelivery to the management server 228. The server 228 may install theprogram into the network processor module 210 for execution by the dataflow processor 310.

Machine learning acceleration hardware 318 may also be preconfigured toprovide acceleration of behavioral analysis computations and processing,or it may be configured through the management server 228. As hereindescribed, the machine learning acceleration hardware 318 may be an FPGAor similar programmable logic that may be configured to perform any of anumber of machine learning acceleration functions. In an example andwithout limitation, the flow processing facility 102 may include aplurality of network processing modules 210, each containing a data flowprocessor 310 and a machine learning acceleration hardware 318. Eachmachine learning acceleration hardware 318 may be an FPGA that may beloaded with logic to analyze a subset of possible protocol layer packetpayloads. Therefore a packet may be directed to one or more of theplurality of network processing modules 210 of the flow processingfacility 102 based on the content of the packet payload.

The content search logic 312 may be used to facilitate content matchingof packet payloads. This logic 312 may perform hardware based regularexpression matching of packet payloads using one or more of thetechniques herein described as associated with the content search logic312. The content search logic 312 may coordinate with other resources ofthe data flow engine 308 such as the cell generator 404 to facilitatecontent matching across multiple packets. Content searching logic 312may perform content matching based on one or more action rules 450 orsecurity policy 414.

Referring again to FIG. 31, network security may be performed at thenetwork layer 3114 although it will be appreciated from the presentdisclosure that there are advantages to performing network security atthe application layer 3110. In particular, it will be appreciated thatcontent inspection of application layer 3110 packets may revealintrusions or threats to a network that are not detectable at a lowerprotocol layer.

The flow processing facility 102 may facilitate content inspection asapplied in a unified threat management application targeting the networklayer. In addition to detecting abnormalities in a network layer packetheader, content inspection of a network layer packet payload may revealproblems that can be addressed by the UTM application. In an example,the content search logic 312 of the flow processing facility 102 may beused to inspect the payload of a network layer packet to detect stringsthat may match a form of invalid application layer packet header. Anetwork layer packet with such a violation may be acted upon by the UTMapplication to prevent the packet from reaching the network, and any andall connection or data flow 444 associated with the packet may beterminated or dropped.

Another form of intrusion that may not be detectable by network securitymethods that inspect only packet headers is a computer virus. Packetsmay contain malicious code, HTTP links, and other data that may beassociated with a virus. Such data may not affect the packet header andtherefore may not be detectable when inspecting an application layerpacket header. Such intrusions may be detected with content inspectionas facilitated by the flow processing facility 102. As described herein,the flow processing facility 102 may process packets such that resourcesof the network processor module 210 and the application processor module212 may be used to inspect packet payloads. In this way, flow processingfacility 102 resources such as the content search logic 312 may beconfigured to compare a payload against a wide variety of threats.Likewise cell router 410 may apply security policy 414 and payloadaction rule 454 to determine if a cell contains or is associated with apacket that appears to include a threat. If so, the cell router 310 mayrespond by routing the cell (or the data flow 444 that is associatedwith the cell) to an appropriate application processor module 212 forfurther processing. By applying action rule 454, the cell router 410may, in embodiments, identify cells that appear to contain or beassociated with packets that harbor or are themselves associated with acomputer virus. In any case, the elements of the application processormodule 212, such as and without limitation the application processingunit 502, may further inspect the content. This further inspection maybe directed at identifying content that may be associated with acomputer virus and taking an appropriate action (such as and withoutlimitation, dropping the packet). It will be appreciated that any andall forms of intrusion, misuse, abuse, undesirable or illegal conduct,and so forth may be detected, processed, and remedied according to thesystems and methods described in this paragraph, this disclosure, andelsewhere.

In an example, the payload action rule 454 may direct the cell router410 in routing any and all cells that are associated with a protectedsystem resource (such as and without limitation a database) to theapplication processor module 212. The application processing unit 502may inspect the payload of one or more packets 402 of the cells todetermine if the system resource reference is threatened by the contentsof the payload. Without limitation, such a threat may be associated withaccessing, modifying, enabling, disabling, impairing, or otherwiseaffecting the system resource.

Additionally or alternatively, content inspection may be applied to ananti-spam campaign at a network level such as at the border of a networkand the internet. The flow processing facility 102 may be used todetermine patterns associated with normal border traffic coming into thenetwork such as email traffic. These patterns may be determined byrouting packets through the data flow processor 310 for behavioralanalysis. By applying the behavioral analysis methods and techniquesherein described to packets containing email, the flow processingfacility 102 may detect email that may be a spam email. In an example,internet email traffic to each recipient on the network may be analyzedfor patterns associated with the source of the email (i.e.: the sender).When spam email begins being delivered to one or more of the recipients,the sender pattern may change in a critical way that is detectable bythe data flow processor 310 or other resources of the flow processingfacility 102. Because this determination may be performed at the borderof the network, the security policy 414 may provide for remedies such asdropping the entire spam email or flagging the packets associated withthe spam, such that when they are delivered to the network mail serverthey can be efficiently routed to a spam folder. By applying anti-spamtechniques such as behavioral based content inspection with the flowprocessing facility 102 to all packets passing through a network border,other communication applications such as instant messaging and faxingmay also be protected from spam.

Content inspection as performed by the flow processing facility 102 mayprovide intrusion detection and prevention services that seek to matchcontent and assess behavior at a plurality of network levels. As anexample and without limitation, a flow processing facility 102 mayinclude a plurality of application processor modules 212 with eachmodule 212 configured to detect intrusions in packet payloads at aspecific network layer. One module 212 may be configured to match packet402 payload contents to known intrusions at the network layer, whileanother may be configured to match packet 402 payload contents to knownintrusions at the application layer. These or other applicationprocessor modules 212 may also analyze the behavior of packet payloadsat the network and application layer respectively. The switching fabric304 of the flow processing facility 102 may switch packets through eachapplication processor module 212 serially or in parallel to provideintrusion detection and prevention at the plurality of protocol layers.By applying the packets to each application processor module 212, theflow processing facility 102 may determine that the packet 402 or flowof related packets 402 may have both a network layer content matchingviolation and an application layer behavioral anomaly. Such informationmay be used to improve network security and performance.

Identifying threats from internal network resources (laptop computersand other mobile computing devices represent known sources of internalnetwork threats) may also be facilitated by inspecting packet payloadcontent with the flow processing facility 102. Network behavior ofinternal network resources may be analyzed by the machine learning logic314 to determine metrics for normal payload content. By routing networktraffic from internal network resources such as mobile computing devicesthrough the flow processing facility 102, anomalies in the patternsgenerated by the payload contents may be detected. As an example andwithout limitation, when a mobile computing device synchronizes with anetwork resource such as an email server or database server, patterns ofpacket payload at the network layer may be readily established. If thepacket 402 payload patterns during synchronization differ criticallyfrom the pattern predicted by the behavioral analysis, the flowprocessing facility 102 may detect the difference and take protectiveaction such as quarantining the device.

Additionally or alternatively, the security policy 414 of the networkmay identify certain types of data to be protected when traveling inpacket payloads over the network. Certain types of data, such as socialsecurity numbers, may be identified in the security policy 414 as beingrestricted to certain destinations on the network. The security policy414 may, for example and without limitation, dictate that a socialsecurity number must be encrypted in a specific way to be transmittedout of the network. By using the flow processing facility 102 to inspectcontent of outgoing packet 402 payloads, unencrypted social securitydata may be detected by the content search logic 312 or cell router 410applying the security policy 414.

Behavioral analysis of packet 402 payloads may not only detect anomaliesin the payloads at various protocol layers, but may also provide ananalysis of behavior of the network that may be beneficial in increasingnetwork security. As the flow processing facility 102 detects anomaliesin packet 402 payloads, the routing information associated with thepackets, such as the source, destination, route taken, and the like maybe determined and analyzed for patterns. By associating packet payloadsthat fail content inspection (content matching or behavioral analysis)with the network routing information, behaviors of the network may bedetermined and used to detect troublesome network activity. In anexample and without limitation, a segment of a network, such as amanufacturing segment containing critical product information, may beassociated with a low occurrence of intrusions. If the intrusion ratefrom the manufacturing segment changes significantly, the flowprocessing facility 102 may issue an alert 442. The alert 442 may directexternal suppliers who connect to the manufacturing segment to providean updated compliance report for the security of their networks as theymay be introducing intrusions or threats that are being detected by theflow processing facility 102 within the manufacturing segment. Suchnetwork behavioral analysis (NBA) and network behavioral anomalydetection (NBAD) may be performed by the flow processing facility 102 aseach new threat is detected, thereby providing an early alert 442 of thenetwork behavioral anomaly.

A network security infrastructure may include a Security EventInformation Management system that may be represented by a variety ofacronyms such as SEIM, SIM, SEM, and SIEM to provide central logging forsecurity events. Security events such as the manufacturing segmentexample described above may be communicated from the flow processingfacility 102 to the management server 228 that may maintain the SEIM.Alternatively, the management server 228 or the flow processing facility102 may send the event information to another server maintaining theSEIM. In this way, even if the flow processing facility 102 were toencounter a failure, the event information would be retained by themanagement 228 or other server.

Payload inspection may also detect network behavioral anomalies that maybe associated with network connections such as ports. By establishingthe normal behavior of packet payloads through a port, a critical changein the behavior detected by analyzing the payloads (such as an increasein the size of payloads or an increase in the number of payloadsdirected toward a port during a normally quite time) may indicate anetwork anomaly such as an intrusion. Because the machine learning logic314 of the flow processing facility 102 may determine “normal” networkactivity for the port for various times, such as times of day or days ofweek, critical changes in the activity as determined by the inspectionof packet payloads through the port can be detected by the flowprocessing facility 102 and action taken such as alerting the managementserver 228 to close the port.

Machine learning logic 314 and the associated machine learningacceleration hardware 318 may apply any or all of the techniques andmethods herein disclosed such as self-organizing maps, neural networks,and others in analyzing the behavior of packet 402 payloads. Thesetechniques may facilitate establishing behavioral criteria associatedwith content inspection of packet 402 payloads processed by the flowprocessing facility 102 independent of the protocol layer at which thepacket payload is processed. The techniques and methods herein disclosedalong with embodiments of the data flow processor 310 may allow the flowprocessing facility 102 to acquire the behavioral criteria intoartificial neurons that may allow for implementation of behavioralanomaly detection in hardware such as the machine learning hardwareaccelerator 318, the cell router 410, or the application accelerator504. The result may be near real-time detection of network behavioralanomalies based on content inspection without relying on queries ofdatabases of ‘normal’ behavior.

Content inspection, when applied across the layers of the IP stack, mayconstitute a unification of a variety of threat management capabilitiesfrom network layer firewalls to application layer data security. UnifiedThreat Management (UTM) as herein described may be facilitated by thetechniques, methods, features, and systems herein described for applyingthe flow processing facility 102 to content inspection. In addition topacket header based behavioral analysis and matching by the flowprocessing facility 102, content matching may be applied to detectthreats within payloads, threats affecting protocols, intrusions passingthrough ports, and attacks on system resources. The flow processingfacility 102 can be configured in a network to inspect content such thatthreats within payloads that can be detected by content matching can beprevented. Threats that compromise the integrity of one or more networkprotocols may be detected by the flow processing facility 102 throughcontent matching of packets associated with the protocol. The networkprocessor module 210 elements and application processor module 212resources may be applied to network traffic to detect protocolcompromising packet payloads as the packets flow through the flowprocessing facility 102 (substantially in real-time). Network trafficassociated with a port may be monitored by the flow processing facility102 with content inspection to ensure any payload destined for the port(or originating in the port) does not include threats, viruses, spam, orother intrusions detectable by applying content matching. Withappropriate security policy 414 defined in the flow processing facility102, system resources such as system files, user passwords, NMS, NEMS,and other key resources may be protected from attack by applying contentmatching to network traffic packet payloads. The resources of the flowprocessing facility 102 such as the network processor module 210elements (e.g. the data flow engine 308, the data flow processor 310,the content search logic 312, the machine learning logic 314, and/or themachine learning acceleration hardware 318) and the applicationprocessor module 212 elements (e.g. the application processing unit 502,and/or in the application accelerator 504) may be configured as hereindescribed to provide a unified threat management solution coveringpacket header and payload inspection.

A network infrastructure may include a Network Management System (NMS)which may include a Network Element Management System (NEMS) responsiblefor the management of at least a portion of the network elements (suchas computers, routers, hubs, network security devices, and the like).The NEMS may communicate with the management server 228 and or the flowprocessing facility 102 to provide network management services. Themanagement server 228 may provide network security related metricsgathered from one or more flow processing facilities 102 to the NEMS forfurther analysis or presentation to a network administrator. In anexample and without limitation, a roll-out campaign for contentinspection may be proceeding with two flow processing facilities 102operating in parallel on the same network traffic wherein one flowprocessing facility 102 is not inspecting content and the other isinspecting content. The NEMS may receive a comparison of the threat andintrusion detection metrics for the two flow processing facilities 102.The management server 228 may communicate with the NEMS throughout thecampaign, receiving updates to security policy 414, new compilation ofprograms to be loaded into the flow processing facility 102 for theapplication processing unit 502, and the like.

In embodiments, the flow processing facility 102 and flow processor 310may be used to process data flows 444 that potentially contain computerviruses, Trojan horses, or similar content. In embodiments, one or moreactions related to threat management, such as virus detection andcleaning may be embodied in the flow processing facility 102 of the flowprocessor 310, as illustrated by examples to follow. In particular, adata flow 444 may be processed by the flow processing facility 102 orflow processor 310 to identify patterns in the data flow 444, such as byusing a set of artificial neurons, such as a neural network or theself-organizing maps described above. Patterns in the data flow 444 maybe recognized that are relevant to identification of a wide range ofthreats to the network, including the threats managed by anti-virusapplications. Thus, as described above and in any of the embodimentsdescribed herein, the flow processing facility 102 may be configured toidentify, and take action with respect to, data flows 444 that containpatterns that suggest the existence of various types of threats,including viruses, as well as Trojan horses and other vehicles forcarrying viruses. In embodiments, the data flow processor 310 describedherein may also include content search logic 312, which may explicitlyimplement pattern recognition using regular expressions (in onepreferred embodiment the pattern recognition is embodied by anoptimization of the Aho-Corasick algorithm). Thus, pattern recognition,in certain preferred embodiments, may consist of applying a set ofartificial neurons such as a SOM or neural net, processing an output ofthe set of artificial neurons (e.g., the fingerprint 448), andperforming a regular expression pattern match on packets of the dataflow 444, or any combination or sub-combination of the same.

The flow processor 310 may thus be configured, applying the patternmatching techniques described above, to assist with matching patternsassociated with viruses and other similar types of threats.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a computer virus. A computer virus as theterm is used herein encompasses any software program, file, computercode, or the like that may infect, damage, or otherwise threaten files,system areas of a computer, network routers, and other computercomponents or resources, such as through what is commonly referred to asa virus vector. Some viruses may be harmless, while others may damagedata files, destroy files, interrupt networking, and or inflict otherdamage to a computer or network. Some viruses may be designed todeliberately damage files, and others may simply spread to othercomputers without damaging files (e.g., a viral propagation of a messagethat is intended to obtain as many viewers of the message as possible).Computer virus vectors of infection may include network shares, softwarevulnerabilities, mass-mailers, worms, internet relay chat, shareddrives, instant messages, infected files, peer-to-peer networks,physical drives, removable drives, floppy drives, spammed email,wireless (e.g., Bluetooth), and other infection vectors. Some computerviruses may require a user action (e.g., opening an email attachment orvisiting a malicious web page) to implement or spread. Other viruses maynot require direct user action (e.g., exploitation of a network'svulnerability to outside access).

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a Trojan horse program. A Trojan horseprogram (sometimes also referred to as “social engineering”) is a typeof virus, or other potentially damaging program, that is embedded,joined, linked, or otherwise associated with a computer program or filethat masks the presence of the virus to the user. A Trojan horse may bea program that purports to do one action (e.g., an mpeg file ostensiblycontaining a video for viewing), when in fact, it has as one of itsactions instructions for performing a malicious action on, or using, acomputer or network. Trojan horses may be included, for example, insoftware downloads, as attachments in email messages, or other filetypes. Because the user downloading a Trojan horse may not know of itspresence, this type of “back door” program may allow intruders access tothe user's computer without the user's knowledge. Through this access,the intruder may be able to change the computer's system configurations,or infect the computer with a virus, or take other actions that areunauthorized by the user. Some Trojan horse programs may not require theuser of a computer to directly download a file (e.g., an emailattachment) in order for the virus to have access to the computer. If auser's email client permits scripting, it may be possible for a Trojanhorse (and its accompanying virus) to load on the user's computer byopening an email message alone. A Trojan program may also employ thetechnique of providing an URL link or download link in order to placeunwanted files onto a computer directly, via a network (e.g., theInternet). This technique may place an added burden on a user's computerinsofar as it may enable an intruder to return to the computer to updatethe virus.

In one example, from many, of a Trojan horse-based virus may come in theform of an email attachment unknowingly downloaded by a computer userand used to launch a denial-of-service attack. This type of attack maycause a computer problems by giving it so many processing instructionsthat the computer is overwhelmed by the data processing volume to suchan extent that it crashes or is otherwise not fully operable. A computermay be a direct target of a denial-of-service attack or it may be usedas a participant in a denial-of-service attack on another system, suchas a server hosting a website targeted by the intruder. Intruders mayuse security compromised computers as platforms for attacking othersystems. In an example, in a distributed denial-of-service attack theintruders may install an agent, such as a Trojan horse program, thatruns on the compromised computer. Once the Trojan horse program isinstalled on the computer, the intruder may issue further instructionsto the computer for it to carry out actions, unbeknownst to the user ofthe computer. Once the intruder has marshaled a sufficient number ofagent computers for a planned attack, he may assign a handler computerthrough which instructions for a denial-of-service attack may beforwarded to the agent computers.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a worm. A worm is another type of computervirus that may spread from computer to computer without users' directinteraction with the computer virus program. Worms may take up valuablememory and network bandwidth, which may cause a computer to stopresponding. Worms may also allow attackers to gain access to yourcomputer remotely. Worm creators may use binary packers to compress theexecutable files associated with viruses, thus making them easier todistribute. The packing process also modifies the internal structure ofa file, which worm creators may use to their benefit. Binary packers maybe used to distribute a worm file that is capable of changing, masked bydozens of different packers. The capability of a worm to change maypermit a virus to have greater longevity, as the virus may be altered tocircumvent security measures taken to combat the virus' firstincarnation upon its introduction.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a virus that is targeted to impact aparticular operating system. Some computer viruses are specificallytargeted to impact a type of computer, computer operating system, orother computer feature. In an example and without limitation,unprotected Microsoft Windows networking shares may be exploited byintruders through automation and used to place tools on MicrosoftWindows-based computers that are connected to the Internet. Because sitesecurity on the Internet or other network may be interdependent, acompromised computer may not only create problems for that computer'suser, but it may also be a threat to other computers, or other locationson a network. Similarly, programming languages, such as Java,JavaScript, ActiveX, and others, that allow web developers to write codethat is executed by a web browser may be used by intruders to gatherinformation (such as visited web sites) or to run malicious code on yourcomputer.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with items sent over a network, such as to a website. A virus creator may attach a script to something sent to the website, such as a URL, an element in a form, or a database inquiry. Later,when the web site responds to a user, the malicious script may betransferred to the user's browser. A computer, computer network, orother network device or network may be exposed to malicious scripts by auser linking to web pages, opening email messages, or newsgrouppostings, and the like without knowing that the action is actuallylinking them to an untrustworthy site.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a various other types of computer virusesand the means for their delivery, including, without limitation, emailspoofing, hidden text file extensions, chat clients, packet sniffing,root kits, bots, and other means of virus delivery.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with email spoofing. Email spoofing refers to thetechnique of making an email message appears to have originated from onesource when it actually was sent from another. Email spoofing may beintended, for example, to instill in the user a false sense of trust inorder to successfully prompt the user into providing the intruder withsensitive information (e.g., passwords or financial information). In anexample and without limitation, an email may claim to be from a systemadministrator requesting a user to change a password to a specifiedstring and threatening to suspend their account if they do not comply.Similarly, an email spoof that is from an authority figure that isrequesting user to send a password file or other sensitive information.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a hidden file extension. Microsoft Windowsoperating systems contain an option to “hide file extensions for knownfile types”. The option may be enabled by default, but a user may chooseto disable this option in order to have file extensions displayed byWindows. Email-borne viruses may exploit these hidden file extensions.In an example and without limitation, a file attached to an emailmessage sent by such a virus may appear to be harmless text (.txt), MPEG(.mpg), AVI (.avi) or other file types, when in fact the file is amalicious script or executable (e.g., .vbs or .exe).

In certain embodiments, pattern matching may be implemented with respectto patterns associated with exploitation of Internet chat applications,instant messaging applications, Internet Relay Chat networks, and thelike, which provide a mechanism for bi-directional data exchange betweencomputers that may be exploited by a computer virus or other damagingcode or file. Chat clients may provide the ability to exchangeexecutable code. This ability may permit an intruder to employ methodsdescribed herein, such as a Trojan horse or spoofing, to presentcomputer virus and the like to unsuspecting users for download.

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a packet-sniffer. A packet sniffer is acomputer program designed to capture data from information packetstraveling over the network. The data from these packets may include usernames, passwords, and other sensitive information traveling over thenetwork. The ability to install a packet sniffer may not requireadministrator-level access. Thus, an intruder may be able to employ apacket sniffer to capture such sensitive information for furtherexploitation (e.g., using a stolen password to access a user's onlinebank account).

In certain embodiments, pattern matching may be implemented with respectto patterns associated with a rootkit. Rootkits are another type ofviral program and may be employed in order to intercept specificApplication Programmer's Interface (API) functions in such a way thatthe information returned by API functions is untrue. A rootkit may usetechniques to gain command of a registry database, process lists, andthe like, in part for the purpose of masking the presence of viralprograms running on the computer, network, or other system. The rootkitmay also be able to mask the registry keys it has modified to furtherminimize detection.

The limitations of the traditional means of controlling and orpreventing viruses through, for example, a stand alone antivirussoftware application may be seen in the example of rootkits. Because ofthe detection shield provided by a rootkit, conventional antivirussoftware may be unable to detect its presence on a computer, computernetwork, or other network. This failure to detect the virus may permitit to remain on a computer, computer network, or other network where itmay continue to perform unwanted and or harmful actions. Furthermore,even in instances where a stand alone software application may beeffective in detecting and or removing an unwanted or harmful file, theapplication may remain cumbersome in its implementation. In an exampleand without limitation, the application may require individualizedinstallation on each client within a network; the application mayrequire frequent updates to be downloaded on each client with a network;differences in client characteristics (e.g., processor speed) mayrequire different antivirus applications to be downloaded on a subset ofclients with in a network, different antivirus applications may containdifferent libraries of viruses for which they scan, resulting invariation of protection levels across the clients within a network, andso forth.

In embodiments, a flow processing facility 102 or flow processor 310 maysecure a computer, computer network, or other network from a virus thatis carried by or associated with a data flow 444. In certainembodiments, the flow processing facility 102 may receive the data flow444 and associate the flow 444 with one or more of a plurality ofanti-virus applications 522, which may reside in one or more of aplurality of application processor modules 212. In other embodiments,the flow processing facility 102 may incorporate or provide anti-virusactions, or it may itself be incorporated into one or more anti-virusapplications. In an example and without limitation, in certainembodiments a data flow 444 from a public network 202 or private network204 may be received by a flow processing facility 102 and handled by theflow processor 310 to produce normalized data 428 from the data flow444. A data cell 408 of the received data flow 444 may indicate thepresence of a computer program. This data cell 408 may optionally berouted through a cell router 410 to an application processor module 212.The application processor module 212 may include an antivirus programthat may be used to analyze the data cell 408 for the presence of avirus or other type of threat described herein. After the applicationprocessor module 212 has analyzed the data cell 408 containing thecomputer program, the data cell 408 may be routed back through the cellrouter 410. If the application processor module 212 determines thepresence of a virus or other suspicious code, the cell router 410 mayroute the data cell 408 to a quarantine facility, at which point theprocessing of the data cell 408 is done 420. Alternatively, the cellrouter 410 may check the data cell 408 against an application ID 412,application group 422, or other identifier 430 or security policy 414 inorder to determine the appropriate future routing of the data cell 408(e.g., whether the data cell, and the program therein, is safe forfurther processing). This flow processing facility architecture may beused to detect viruses, including, but not limited to, associated withnetwork shares, software vulnerabilities, mass-mailers, worms, internetrelay chat, shared drives, instant messages, infected files,peer-to-peer networks, physical drives, removable drives, floppy drives,spammed email, wireless (e.g., Bluetooth), and other infection vectors.This flow processing facility architecture may be used to analyze virusvectors, including, but not limited to, Trojan horses, Windowsnetworking shares, worms, scripts, email spoofing, hidden text fileextensions, chat clients, packet sniffing, root kits, bots, and othermeans of virus delivery.

In embodiments, machine learning may be used to create a self organizingmap capable of detecting anomalies in a data flow 444 for securing acomputer, computer network, or other network from viruses. In an exampleand without limitation, a data flow 444 from a public network 202 orprivate network 204 may be received by a data flow processor 310. Thedata flow processor 310 may include machine learning accelerationhardware 318, machine learning logic 314, content search logic 312, andthe like, that may be used to generate a self organizing map or neuralnetwork that may identify anomalies in the packets 402 associated withthe data flow 444. In embodiments, the flow processor 310 may producenormalized data 428 for further processing and security analysis. Forinstance and without limitation, the normalized data 428 produced by theflow processor 310 through the use of the self organizing map may befurther associated with a normalized data type 424, an application group422 or application ID 412, other identifiers 430, and or a securitypolicy 414 in order to determine the appropriate future routing of thedata cell 408 (e.g., whether the data cell, and the program therein, issafe for further processing). An anomalous data flow 444 that may bedetected by a self organizing map may include, but is not limited to, adata flow 444 from an unknown source, a data flow 444 from a new source,a data flow 444 from an infrequent source, a data flow 444 containing afile type rarely or never received, a data flow 444 containing a filetype rarely or never received from a particular source, a data flow 444exceeding a size, a data flow 444 from an application group 422, a dataflow 444 from a targeted application ID 412, a data flow 444 for atargeted host, a data flow 444 from a targeted host type, a data flow444 from a targeted host location, a data flow 444 including aprogramming language type, a data flow 444 containing a targeted text,and or any other data flow 444 indicator or combination of a pluralityof data flow 444 indicators.

This artificial neuron approach, optionally embodied in a selforganizing map architecture or neural net, may be used to detectviruses, including, but not limited to, ones associated with networkshares, software vulnerabilities, mass-mailers, worms, internet relaychat, shared drives, instant messages, infected files, peer-to-peernetworks, physical drives, removable drives, floppy drives, spammedemail, wireless (e.g., Bluetooth), and other infection vectors. Thisflow processing facility architecture may be used to analyze virusvectors, including, but not limited to, Trojan horses, Windowsnetworking shares, worms, scripts, email spoofing, hidden text fileextensions, chat clients, packet sniffing, root kits, bots, and othermeans of virus delivery.

In embodiments, a flow processing facility 102 may secure a computer,computer network, or other network from a virus that is carried by orassociated with a data flow 444 from a targeted user. A targeted usermay be, but is not limited to, an individual, entity, customer, source,and the like. The flow processing facility 102 may receive the targeteduser's data flow 444 and associate the flow 444 with one or more of aplurality of anti-virus applications 522, which may reside in one ormore of a plurality of application processor modules 212. In an exampleand without limitation, a targeted user's data flow 444 may be receivedfrom a public network 202 or private network 204 by a flow processingfacility 102 enabled to produce normalized data 428 from the targetuser's data flow 444. A data cell 408 of the received data flow 444 mayindicate the presence of a computer program. This data cell 408 may berouted through a cell router 410 to an application processor module 212.The application processor module 212 may include an antivirus programthat may be used to analyze the target user's data cell 408 for thepresence of a virus. After the application processor module 212 hasanalyzed the targeted user's data cell 408 containing the computerprogram, the data cell 408 may be routed back through the cell router410. If the application processor module 212 determines the targeteduser's data flow 444 contains a virus or other suspicious code, the cellrouter 410 may route the data cell 408 to a quarantine facility, atwhich point the processing of the data cell 408 is done 420.Alternatively, the cell router 410 may further check the targeted user'sdata cell 408 against other identifiers 430, security policies 414, andthe like in order to determine the appropriate future routing of theuser's data cell 408. Once the targeted user's normalized data 428 hasbeen determined to be free of viruses it may be routed to a packetgenerator 418 for forwarding on to a network (e.g., a physical networkinterface 302).

In embodiments, machine learning may be used to create a self organizingmap capable of detecting anomalies in a targeted user's data flow 444for securing a computer, computer network, or other network fromviruses. In an example and without limitation, a targeted user's dataflow 444 may be received from a public network 202 or private network204 by a data flow processor 310. The data flow processor 310 mayinclude machine learning acceleration hardware 318, machine learninglogic 314, content search logic 312, and the like, that may be used togenerate a self organizing map associated with the targeted user. Theself organizing map may be able to identify anomalies in the packets 402associated with the targeted user's data flow 444, and producenormalized data 428 for further processing and security analysis. Forinstance and without limitation, the targeted user's data flow 444 maybe anomalous in that it exceeds the data format associated with theuser. The normalized data 428 produced through the use of the selforganizing map may be further associated with a normalized data type424, an application group 422, other identifiers 430, and/or a securitypolicy 414 in order to determine the appropriate future routing of thetargeted user's data cell 408 (e.g., whether the data cell, and theprogram therein, is safe for further processing). Once the targeteduser's normalized data 428 has been determined to be free of viruses itmay be routed to a packet generator 418 for forwarding on to a network(e.g., a physical network interface 302).

In embodiments, a flow processing facility 102 may secure a computer,computer network, or other network from a virus that is carried by orassociated with a data flow 444 from a targeted host. A targeted hostmay be, but is not limited to, a server, network host, ISP, entity, andthe like. The flow processing facility 102 may receive the targetedhost's data flow 444 and associate the flow 444 with one or more of aplurality of anti-virus applications 522, which may reside in one ormore of a plurality of application processor modules 212. In an exampleand without limitation, a targeted host's data flow 444 may be receivedfrom a public network 202 or private network 204 by a flow processingfacility 102 enabled to produce normalized data 428 from the targethost's data flow 444. A data cell 408 of the received data flow 444 mayindicate the presence of a computer program. This data cell 408 may berouted through a cell router 410 to an application processor module 212.The application processor module 212 may include an antivirus programthat may be used to analyze the target host's data cell 408 for thepresence of a virus. After the application processor module 212 hasanalyzed the targeted host's data cell 408 containing the computerprogram, the data cell 408 may be routed back through the cell router410. If the application processor module 212 determines the targetedhost's data flow 444 contains a virus or other suspicious code, the cellrouter 410 may route the data cell 408 to a quarantine facility, atwhich point the processing of the data cell 408 is done 420.Alternatively, the cell router 410 may further check the targeted user'sdata cell 408 against other identifiers 430, security policies 414, andthe like in order to determine the appropriate future routing of thehost's data cell 408. Once the targeted host's normalized data 428 hasbeen determined to be free of viruses it may be routed to a packetgenerator 418 for forwarding on to a network (e.g., a physical networkinterface 302).

In embodiments, machine learning may be used to create a self organizingmap capable of detecting anomalies in a targeted host's data flow 444for securing a computer, computer network, or other network fromviruses. In an example and without limitation, a targeted host's dataflow 444 may be received from a public network 202 or private network204 by a data flow processor 310. The data flow processor 310 mayinclude machine learning acceleration hardware 318, machine learninglogic 314, content search logic 312, and the like, that may be used togenerate a self organizing map associated with the targeted host. Theself organizing map may be able to identify anomalies in the packets 402associated with the targeted host's data flow 444, and producenormalized data 428 for further processing and security analysis. Forinstance and without limitation, the targeted host's data flow 444 maybe anomalous in that the source of the data flow 444 is other than thatassociated with the host in the self organizing map. The normalized data428 produced through the use of the self organizing map may be furtherassociated with a normalized data type 424, an application group 422,other identifiers 430, and/or a security policy 414 in order todetermine the appropriate future routing of the targeted host's datacell 408 (e.g., whether the data cell, and the program therein, is safefor further processing). Once the targeted host's normalized data 428has been determined to be free of viruses it may be routed to a packetgenerator 418 for forwarding on to a network (e.g., a physical networkinterface 302).

In embodiments, a flow processing facility 102 may secure a computer,computer network, or other network from a virus that is carried by orassociated with a data flow 444 from a targeted application type. Atargeted application type may be, but is not limited to, an emailapplication, Java application, Bluetooth application, open sourceapplication, and the like. The flow processing facility 102 may receivethe targeted application data flow 444 and associate the flow 444 withone or more of a plurality of anti-virus applications 522, which mayreside in one or more of a plurality of application processor modules212. In an example and without limitation, a targeted application dataflow 444 may be received from a public network 202 or private network204 by a flow processing facility 102 enabled to produce normalized data428 from the target application data flow 444. A data cell 408 of thereceived data flow 444 may indicate the presence of a computer programmade by the targeted application type. This data cell 408 may be routedthrough a cell router 410 to an application processor module 212. Theapplication processor module 212 may include an antivirus program thatmay be used to analyze the target application data cell 408 for thepresence of a virus. After the application processor module 212 hasanalyzed the targeted application data cell 408 it may be routed backthrough the cell router 410. If the application processor module 212determines the targeted application data flow 444 contains a virus orother suspicious code, the cell router 410 may route the data cell 408to a quarantine facility, at which point the processing of the data cell408 is done 420. Alternatively, the cell router 410 may further checkthe targeted application data cell 408 against other identifiers 430,security policies 414, and the like in order to determine theappropriate future routing of the application data cell 408. Once thetargeted application normalized data 428 has been determined to be freeof viruses it may be routed to a packet generator 418 for forwarding onto a network (e.g., a physical network interface 302).

In embodiments, machine learning may be used to create a self organizingmap capable of detecting anomalies in a data flow 444 from a targetedapplication type for securing a computer, computer network, or othernetwork from viruses. In an example and without limitation, a targetedapplication data flow 444 may be received from a public network 202 orprivate network 204 by a data flow processor 310. The data flowprocessor 310 may include machine learning acceleration hardware 318,machine learning logic 314, content search logic 312, and the like, thatmay be used to generate a self organizing map associated with thetargeted application type. The self organizing map may be able toidentify anomalies in the packets 402 associated with the targetedapplication data flow 444, and produce normalized data 428 for furtherprocessing and security analysis. For instance and without limitation, atargeted application data flow 444 may be a data flow associated withthe Java application type. A Java application type data flow 444 may beanomalous in that it contains non-standard Java code, embedded code,code type hybrids, and so forth. The normalized data 428 producedthrough the use of the self organizing map may be further associatedwith a normalized data type 424, an application group 422, otheridentifiers 430, and or a security policy 414 in order to determine theappropriate future routing of the targeted application data cell 408(e.g., whether the data cell, and the program therein, is safe forfurther processing). Once the targeted application normalized data 428has been determined to be free of viruses it may be routed to a packetgenerator 418 for forwarding on to a network (e.g., a physical networkinterface 302).

In embodiments, a flow processing facility 102 may secure a computer,computer network, or other network from a virus that is carried by orassociated with a data flow 444 from a targeted file type. A targetedfile type may be, but is not limited to, an email, executable file,.jpeg, .mpeg, and the like. The flow processing facility 102 may receivethe targeted file data flow 444 and associate the flow 444 with one ormore of a plurality of anti-virus applications 522, which may reside inone or more of a plurality of application processor modules 212. In anexample and without limitation, a targeted file data flow 444 may bereceived from a public network 202 or private network 204 by a flowprocessing facility 102 enabled to produce normalized data 428 from thetarget file data flow 444. A data cell 408 of the received data flow 444may indicate the presence of a computer program with the targeted filetype. This data cell 408 may be routed through a cell router 410 to anapplication processor module 212. The application processor module 212may include an antivirus program that may be used to analyze the targetfile data cell 408 for the presence of a virus. After the applicationprocessor module 212 has analyzed the targeted file data cell 408 it maybe routed back through the cell router 410. If the application processormodule 212 determines the targeted file data flow 444 contains a virusor other suspicious code, the cell router 410 may route the data cell408 to a quarantine facility, at which point the processing of the datacell 408 is done 420. Alternatively, the cell router 410 may furthercheck the targeted file data cell 408 against other identifiers 430,security policies 414, and the like in order to determine theappropriate future routing of the targeted file data cell 408. Once thetargeted file normalized data 428 has been determined to be free ofviruses it may be routed to a packet generator 418 for forwarding on toa network (e.g., a physical network interface 302).

In embodiments, machine learning may be used to create a self organizingmap capable of detecting anomalies in a data flow 444 from a targetedfile type for securing a computer, computer network, or other networkfrom viruses. In an example and without limitation, a targeted file dataflow 444 may be received from a public network 202 or private network204 by a data flow processor 310. The data flow processor 310 mayinclude machine learning acceleration hardware 318, machine learninglogic 314, content search logic 312, and the like, that may be used togenerate a self organizing map associated with the targeted file type.The self organizing map may be able to identify anomalies in the packets402 associated with the targeted file data flow 444, and producenormalized data 428 for further processing and security analysis. Forinstance and without limitation, a targeted file data flow 444 may be adata flow associated with an email file type. An email file data flow444 may be anomalous in that it contains an executable file, .jpeg, orother code. The normalized data 428 produced through the use of the selforganizing map may be further associated with a normalized data type424, an application group 422, other identifiers 430, and/or a securitypolicy 414 in order to determine the appropriate future routing of thetargeted file data cell 408 (e.g., whether the data cell is safe forfurther processing). Once the targeted file normalized data 428 has beendetermined to be free of viruses it may be routed to a packet generator418 for forwarding on to a network (e.g., a physical network interface302).

All of the elements of the flow processing facility 102 and relatedanti-virus features may be depicted throughout the figures with respectto logical boundaries between the elements. According to software orhardware engineering practices, the modules that are depicted may infact be implemented as individual modules. However, the modules may alsobe implemented in a more monolithic fashion, with logical boundaries notso clearly defined in the source code, object code, hardware logic, orhardware modules that implement the modules. All such implementationsare within the scope of the present disclosure.

The flow processing facility 102 may provide a service generallyassociated with a network firewall; may be incorporated in a networkfile; and/or may be associated with a network firewall. Aself-organizing map or other machine learning logic 314 may,respectively, detect an anomalous data flow 444 and process the dataflow 444 to check for and, perhaps, remedy network attacks or threats,which may include intentional or unintentional malformations of the dataflow 444, repetitions in the data flow 444, multiple transmissions ofthe data flow 444, and so forth. A firewall application 514 may processa data flow 444 (including its packet headers and/or payloads), checkingits data cells 408 for known attacks, malformed headers, suspiciouspayloads, and so forth. The firewall application 514 may or may notprovide stateful inspection of the data cells 408. The firewallapplication 514 may allow, deny, or modify the data flow 444, asappropriate and as is described in greater detail hereinafter. Thefirewall application 514 may employ content inspection.

It will be appreciated that using a self-organizing map to provideanomaly detection on the data flow 444 may provide advantages. In anexample and without limitation, the flow processing facility 102 maydirect only anomalous data flows 444 to the firewall application 514.Since any and all data flows 444 that are not anomalous might not bedirected to the firewall application 514, computing resources areconserved as compared with a system in which all of the data flows 444are directed to the firewall application 514. Moreover, theself-organizing map may be trained on recent data flows 444, so that itsrepresentation of what is and is not anomalous may be relativelycurrent. This may be important since network conditions such asthroughput, inter-arrival times, and other factors may changedramatically over time. Thus, the flow processing facility 102 may becapable of accurately classifying data flows 444 as anomalous, even inthe face of changing network conditions. Many other advantages will beapparent.

A firewall may refer to a system or group of systems comprising one ormore software programs and/or hardware devices that, when integratedinto a networked environment 100, implement one or more measures todetect, prohibit, circumscribe, and/or otherwise limit communicationsthat are disallowed, such as and without limitation by a referencenetwork security policy 414. Such a policy 414 may consist ofinformation concerning the conditions (if any) under which a facilitythat is interacting with a network may be granted to access to and/orfrom network resources, facilities, services, devices, and the like. Anetworked environment 100 may be composed of one or more computers (suchas and without limitation the server computing facilities 108) that maybe operatively coupled to one or more computers via a data communicationsystem, which may consist of the internetwork 104, the flow processingfacility 102, and so forth.

The term “network firewall” may be used interchangeably with the terms“packet filter” and “border security device”, but formally may refer toone or more systems, devices, or combinations thereof that controlaccess to and from a network by examining elements of a data flow 444that may be associated with a layer of a protocol stack, which mayconsist of an OSI-compliant protocol stack, an Internet protocol stack,or any other protocol stack. In embodiments, the network firewall may bedirected at the network layer of a OSI-compliant protocol stack (Layer3), which may encompass TCP/IP. In any case, the communication mayconsist of packets 402 that may originate from and/or may be directed tofacilities within the a protected network, application, service, orother element of a networked computing environment 100. The networkfirewall may inspect and filter a packet 402 or associated data cell 408according to a criterion or rule 450, which may be associated with oneor more access policies. This rule 450 may factor such Layer 3components as source and destination addresses, port access information,other semaphore elements, and so forth.

The term “network firewall” may refer to one or more system or devicesthat control access to and/of from clients and resources within anetwork by examining the network-level components of a layeredcommunication protocol (where these components may reside in Layer 3 ofthe OSI communication model). An “application-layer firewall” mayexamine the payload of a packet 402 or associated data cell 408 and maybe directed at elements above Layer 3 and particularly at elementsassociated with Layer 7, the application layer. An application-layerfirewall may process and respond to a data flow 444 and associatedpackets 402 or data cells 408 according to a different set of criteriathan a network firewall. Examples of application-layer firewallsinclude, but are not limited to, anti-virus facilities, anti-spamprograms, pop-up blockers, and other such content-based, behavior-based,anomaly-based, flow-based, rule-based, or other data flow 444 processingfacilities.

A network firewall may be operated in parallel to or in series with anapplication-layer firewall. When deployed in this way, the networkfirewall may comprise a component in a Unified Threat Management (UTM)system, which may include an application-layer firewall and/or any andall other security facilities. In a parallel configuration, a packet 402or groups of packets 402 (or one or more data cells 408 associatedtherewith) that are in violation of a network-layer security policy(which may be an instance of the security policy 414) may be routed toan plurality of adjunctive facilities (such as and without limitation aplurality of applications 512) for further, parallel examination orprocessing. In a series configuration, either the network firewall orthe application-layer firewall (the order of the cascade may vary) mayfirst process a packet 402 or associated data cell 408 and then route itto the succeeding stage. Finally, a network firewalls may be operated ina standalone fashion; that is, operated without any parallel systems oradjunctive facilities.

One aim of firewall protection may be to shield a computer or networkfrom a communication or data flow 444 that would be harmful to thatcomputer or network. The subject data flow 444 may originate from apublic network 202, a private network 204, a server computing facility108, an internetwork 104, a computing facility that is associated withone or more of the foregoing, and so forth. A network firewall mayexamine the structure, formation, source, destination, or other suchelement of associated with a data flow 444. This examination may bedirected at detecting communications that, if allowed to pass unhinderedthrough the network firewall, may impair the proper operation of anetwork that is operatively coupled to the network firewall and/or acomputing facility that is operatively coupled to the network and/ornetwork firewall. Such an impairment may, without limitation, includedamage to or degradation of a service provided by the network;corruption or disruption of processes within the computing facility;corruption of and/or damage to data within the network or computingfacility; breach or compromise of confidentiality or integrity of thenetwork, the computing facility, data transmitted through the network,data stored in the computing facility; and so forth.

A network firewall may provide protection from many types of attacks,some of which may be intentional and malicious, some of which may be theresult of a malfunctioning or rogue facility. Any and all of theseattacks may attempt to create a disruption by one or more of thesetechniques selected from the following group of techniques: consumeresources used in data communication (such as bandwidth, disk space, orCPU time); intentionally mangling or otherwise manipulatingconfiguration information related to network communication (such asrouting information); creating a disruption of physical networkcomponents; and so forth.

In an example and without limitation, a denial-of-service (DoS) attackmay comprise a malicious or intentional communication attack againstwhich a network firewall may provide protection. One kind of DoS attackis referred to in the art as flooding. Flooding may consist of a bruteforce attempt to monopolize network and/or computational resources bysubmitting a large number of packets 402 to a destination with theintent of overwhelming a computing facility at the destination andcausing a disruption. Flooding may use one or more elements in thenetwork layer to initiate, introduce, or reinforce the disruption.

An example of flooding is known in the art as a “SYN flood,” which maygenerate a flurry of TCP SYN packets 402—where a SYN packet 402 may be asynchronization packet 402 for requesting a TCP connection—from aninvalid sender address. An unprotected target facility that interpretsthese packets 402 as being valid requests to initiate TCP connectionswould open a connection with the source of the packet, returning anacknowledgement (a TCP/SYN-ACK packet 402). However, since the sourceaddress is invalid, no response would be forthcoming. As a result, alarge number of unresolved connections may remain oven on the targetfacility, consuming system resources so as to impair the ability of thetarget facility to responding to legitimate requests. Other examples offlooding may consist of a SYN flood and other techniques, such as “pingflooding” in a distributed manner or other so-called distributed,denial-of-service attacks (DDOS) wherein multiple computing facilitiesmount a more or less simultaneous attack on one or more targets.

A “smurf” attack is another example of a DoS flooding attack. In thiscase, a client within a network is co-opted and floods other clients onthat network with packets 402 (which are made to carry the address ofthe co-opted client as the source) using a broadcast address of thenetwork as the destination. The other clients may monitor the broadcastaddress in addition to their own unique address. In a variation of thisattack, a banana attack uses a co-opted client to generate packets 402(typically ICMP packets) with that client's own address as thedestination, thereby consuming network bandwidth and routing resources.A further variation of the smurf attack is called a “fraggle” attack,which uses the echo facility of UDP to flood the broadcast address of anetwork, using either an invalid or co-opted source address.

Another kind of attack may be referred to in the art as a “nuke” attack.In this case, a malicious resource sends a damaged or malformed packetto a target (often via ICMP) that is directed at exploiting a weaknessin the operating system of the target. In one example, a “bomb” may beaddressed to a certain logical network port of a target computingfacility and may contain invalid information that, when received, maycause the target to crash, to operate in an impaired mode or fashion, orto impair another computing facility. In one example, unrelated logicalnetwork ports that are otherwise available for other services may beblocked or changed. Some types of nukes are directed at a server towhich other computers may attach: when a malicious packet is received atthe server, the server may not be able to service requests from itsclients or may return nonsensical or harmful data.

One variation of the nuke attack may be known in the art as a “teardrop”attack. Here, a malicious source may exploits a bug in an older Windowssystem by sending fragments that are spread across packets withoverlapping payloads. The design of the packets may induce the systeminto incorrectly reassembling the fragments, causing the system tocrash.

Other attacks may use an element of the network layer inappropriately orout-of-context. In one case, a packet 402 may be transmitted to adestination facility wherein an URG flag within the packet may be set.The URG flag may be a TCP flag signaling that that packet 402 is to beprocessed immediately. A queuing operation of the destination facilitymay be impaired or otherwise affected by such a packet 402.

A firewall may be configured in a variety of ways, including (but notlimited to) a system that provides protection for a local area network(LAN); a system that is deployed by an ISP and that provides Internetaccess to subscribers, computing facilities, networks, and so forth; anindividual computing facility that access a network facility; and soforth.

In the preferred embodiment, a network firewall may monitor a data flow444 consisting of one or more IP-based data packets 402. In alternateembodiments, the network firewall may monitor any and all othercommunication structures that may be overlaid above a link layer such asEthernet. These protocols may include (but are limited to) IP; TCP/IP;UDP/IP; IPSec; SSH; SCP/SSH; DHCP; BGP; SMTP; ICMP; NNTP; NTP; LDAP;IGMP; RTTP; ARP; and so on.

It will be appreciated that network attacks may be randomized and/orpermuted in ways that may be directed at avoiding detection by a networkfirewall. For this reason, security policies in a network firewall mayrequire a need to be dynamic, adaptive, and/or updated on a regularbasis to keep pace with new attacks and new variations of older ones.

In embodiments, a flow processing facility 102 may protect a computingfacility and/or network facility from a network attack by examining adata flow 444 that is received from a public network 202 and/or from aprivate network 204. The flow processing facility 102 may comprise oneor more application modules 512, which themselves may encompass one ormore firewall applications 514. The firewall applications 514 may beassociated with one more applications 512, which may be co-resident inthe application modules 512.

In embodiments, the network processor module 210 may receive a data flow444 through any of the physical interfaces 302 as described hereinabovewith reference to FIG. 3. The data flow 444 may then be provided to adata flow engine 308. There, a data packet 402 that is associated withthe data flow 444 may be presented to a cell generator 404 or otherformatting stage, which may transform the packet 402 into a data cell408 for presentation to a cell router 410. Additionally, portions of thepacket 402 that may relate to the firewall application 514 may beprocessed into normalized data 428. Furthermore, an applicationidentifier 412 of the firewall application 514 may be associated withthe data packet 402. The cell router 410 may consider this applicationidentifier 412 when determining where to route a data cell 408 that isassociated with the packet 402.

In embodiments, a data flow 444 that contains an IP SYN may be receivedby a flow processing facility 102 from a public network 202 or privatenetwork 204. In an example and without limitation, the IP SYN mayencompass a request by a client computing facility within Eng Dept 110and to which a reply, in accordance with TCP, may be in order. The dataflow processor 310 may communicate an application identifier 412 to thecell router 410, wherein the identifier 412 may be associated with apacket 402 of the data flow 444 that contains the SYN. The cell router,in light of the application identifier 412, may direct one or more datacells 408 that are associated with the packet 402 to one or moreinstances of a firewall application 514. These instances of the firewallapplication 514 may analyze the SYN request that may be within datacells 408 for the presence of anomalous, repetitious, and/or malformeddata that may indicate that the SYN request is malicious or erroneous.The presence of repeated SYN requests over a short period of time fromthe same address may be indicative that SYN flooding attack is underway.

The flow processing facility 102 may include one or more firewallapplications 514, which may be used, as described throughout thisdocument, to protect against network attacks including (but not limitedto) DoS attacks and their variants, DDoS attacks and their variants,bombs, nukes, and other such attacks wherein one or more packets 402 areintentionally or unintentionally malformed, sequenced, repeated,damaged, mangled, or otherwise directed at producing an ill effect on acomputing facility or network facility.

In embodiments, the methods and systems disclosed herein may provide aflow processing facility for processing a data flow, and configuring theflow processing facility to recognize patterns in the data flow based atleast in part on learning (e.g., artificial neurons, an SOM-based neuralnet, and the like).

In embodiments, the data flow processor 310 may incorporate unifiedthreat management functionalities that are relevant to identifyingthreats of disparate types, including threats relevant to intrusiondetection, intrusion protection, anti-virus protection, anti-spywareprotection, and anti-spam protection, as well as other types of threats,such as related to phishing or unauthorized use of computer networkresources. In other embodiments, the data flow processor 310 may beincorporated within a unified threat management application such thatthe data flow processor 310 functionality is one of a plurality offunctionalities provided by the unified threat management application.In other embodiments, the data flow processor 310 may be independentfrom, but associated with, a unified threat management application suchthat the identification of disparate threat types described above hereinis provided by the data flow processor 310 in conjunction with anindependent unified threat management application, or the like.

In embodiments, an indication suggestive of an attack may be detected bya machine learning logic 312, such as and without limitation aself-organizing map. The machine learning logic 314 and/or parametersthereof may be generated by one or more components of a data flow engine308, may be imported from another machine learning logic 314, may be theresult of a machine-learning algorithm, or may result from a combinationof the foregoing. In embodiments, elements or parameters of aself-organizing map may be updated, refreshed, or otherwise modified, ona continuing or discrete basis, by a machine-learning algorithm, whichmay or may not reside in the data flow engine 308. Alternatively oradditionally, the self-organizing map or a process associated with itmay import or otherwise obtain adjunctive, additional, or revisedelements or parameters from another self-organizing map. Theself-organizing map may also be updated, refreshed, and/or otherwisemodified, on a continuing or discrete basis, by co-resident modules(and/or by processes associated with such co-resident modules). In anexample and without limitation, an anti-virus application 522 may passan alert to or otherwise inform a resident firewall application 512 thatit has identified a data flow 444 as carrying malicious code. Such analert may include relevant elements of (and/or information about) thedata cells 408 or data packets 402 that are associated with themalicious code.

The firewall application 512 may identify an anomalous activity within adata flow 444 by detecting the degree to which (or, the number of numberof times that) the data flow 444 maps to an artificial neuron in theself organizing map. It will be appreciated that the machine learninglogic 314 or self-organizing map may detect an anomaly that emerges overtime and that may not be evident in a single event, packet 403, or cell408. When such an anomaly is detected, it may be indicated in thenormalized data 428, which may be associated with an applicationidentification 412 and a security policy 414, either or both of whichmay be associated with directing the cell router 410 to transmit any andall data cells 408 that are associated with the anomaly to an instanceof the firewall application 514. The firewall application 514 mayexamine these data cells 408, either on line or off line, to determinewhether or not the detected anomaly represents a correct detection. Ifit does, the firewall application 514 may take an action, such asmodifying or dropping one or more of the data cell 408, so that no harmor ill effect is brought upon a computing facility or network facilityby the data cell 408.

The anomaly may derive from, but may not be limited to, a data flow 444from an unknown source or to an unknown destination; a data flow 444from a new infrequent source; a data flow 444 to a new or infrequentdestination; a data flow 444 with or without any of the foregoing sourceand destination combinations; a data flow 444 that contains an IP flag,function, or other semaphore that may be associated with a particularattack; a data flow 444 that contains a flag, function, or othersemaphore is rarely or never before received; a data flow 444 containinga component that is rarely or never before received from a particularsource; a data flow 444 that is malformed or damaged; a data flow 444that is addressed to or from a particular location; a data flow 444 thatis addressed to one or more ports on a computing facility or networkfacility that are associated with an attack; and so forth.

In an example and without limitation, one or more self-organizing mapsmay have been trained to detect the emergence of a fraggle attack. Inthis attack, a UDP/IP packet 402 may be sent from a source to adestination computing facility. The packet 402 may comprise a requestthat the target computing facility “echo” the packet. The packet 402 maybe transmitted using the broadcast address of the network within whichthe target resides. A security policy 414 may specify, generally,whether such a request should be allowed to pass through the data flowengine 308. However, some requests of this type may be valid (there arevalid communications that use UDP/IP echoing) so simply prohibiting thistype of communication may not be desirable. Instead, each and everypacket 402 that is received by the data flow engine 308 may be mapped toa feature vector. These feature vectors may themselves be mapped toartificial neurons within a self-organizing map. A detection thresholdmay be set for some or all of the artificial neurons of theself-organizing map. If the number of feature vectors that map to aparticular neuron exceeds the detection threshold for that neuron thenthe data flow 444 may be flagged as anomalous. If a security policy 414that is associated with the data flow 444 indicates that such an anomalyis cause for further processing, and if an application identifier 412associated with the data flow refers to a firewall application 514, thenfrom that point and until the data flow 444 is no longer flagged asanomalous, any and all of the data cells 408 associated with the dataflow 444 may be routed by the cell router 410 to the firewallapplication 514 in an application processor module 212. Depending upon adetermination or action of the firewall application 514, the data flow444 may be quarantined, dropped, modified, inhibited, allowed, denied,or otherwise controlled.

In the event that the firewall application 512 detects an attack, analert 442 may be generated. As with other elements in a UTM environment,this alert may take the form of a data element, an electric signal, anaudible or visible annunciation, a wireless signal, or a communicationstream, some combination of the foregoing, and so forth. The alert 442may also be routed to another element of the flow processing facility102, such as another component of an application processor module 212.The alert 442 may also signal a human operator of the flow processingfacility 102, and/or any other facility, program, or device that isconfigured to receive and process the alert 442. In the present exampleof a fraggle attack, an alert 442 may be passed to anti-spam application528 so that the source address of the attack may be integrated into adatabase or set of references used by that application 528.

All of the elements of the flow processing facility 102 and firewallapplication 514 may be depicted throughout the figures with respect tological boundaries between the elements. According to software orhardware engineering practices, the modules that are depicted may infact be implemented as individual modules. However, the modules may alsobe implemented in a more monolithic fashion, with logical boundaries notso clearly defined in the source code, object code, hardware logic, orhardware modules that implement the modules. All such implementationsare within the scope of the present invention.

Firewalls 514 are known to provide external access control andfiltering. Embodied as a firewall 514, intrusion detection andprevention may act as a perimeter guard for a network, determining whattraffic to allow or deny in and out. A firewall 514 may do this byapplying a policy, which may comprise accept and deny rules, based onvarious criteria, such as a source, destination, and protocol. Byproviding access control, a firewall 514 may provide a first layer ofdefense to external intrusions. A firewall policy may allow protocolsthat enable organizations to do business on the Internet, such as SMTP,FTP, HTTP, SMTP and DNS, and may keep out some traffic that may pose athreat to the internal systems. As herein disclosed, flow processingfacility 102 may include a firewall 514 application and therefore mayfacilitate intrusion detection and prevention.

Referring to FIG. 29, a schematic depicting an example networkedcomputing environment 2900 in which intrusion detection and preventionis employed; various locations of intrusion detection and prevention areshown. The network of FIG. 29 includes a firewall 514; servers 108;virtual private network port 2910; segments 2912, 2914; a wireless port2918; users 2920; and a VoIP port 2922.

Those skilled in the art will appreciate that the example networkedcomputing environment 2900 is simplified for pedagogical purposes. In anexample and without limitation, the environment 2900 does not show theplurality of networking devices that externally connect to the firewall514, the various hubs, routers, and switches that may comprise thenetworked computing operation of an actual enterprise, and so on. Thesesimplifications are provided for the purpose of drawing attention to theintrusion detection and prevention facility 2902, which is an object ofthe present invention. However, given that networked computingenvironments 2900 can be arbitrarily complex and assume a countlessnumber of configurations, the deployment of the intrusion detection andprevention facility 2902 is not in any way limited to the particularnetworked computing environment 2900 shown here.

The intrusion detection and prevention facility 2902 may be anindependent platform as shown in FIG. 29. Such a facility 2902 mayidentify and prevent intrusions by examining network traffic through oneor more connections of a network. This examination of network trafficmay encompass content inspection and/or processing packet headers.Intrusion detection and prevention facility 2902 may gain access to thenetwork traffic by interconnecting a network and a firewall 514, asegment such as eng segment 2914 or mfg segment 2912, users 2920, avirtual private network 2910, a server 108, or a wireless port 2918, andany other networked device, facility, port, hub, router, switch, and soon. In such a configuration, the intrusion detection and preventionfacility 2902, also called a sensor, may capture network traffic flowsfor analysis of the content and packets for malicious traffic.

Intrusion detection and prevention 2902 may be configured in a flowprocessing facility 102 and operate in one or more modes including,without limitation hub mode, tap mode, port clustering mode, and in-linemode. In a hub mode network configuration the flow processing facility102 may be connected to a SPAN port of a network switch device or anetwork hub device processing data streams in parallel to the switch orhub. In a tap mode network configuration the flow processing facility102 may be configured in-line with network traffic yet may befunctionally disabled without disrupting network traffic by allowingnetwork traffic to passively transfer through the device. Such a modemay be appropriate for interconnection to a segment 2912. In portclustering mode a plurality of network traffic streams may be combinedfor intrusion analysis, detection, and prevention. Examples of portclustering shown in FIG. 29 include interconnecting users 2920, andsegments 2912 and 2914. In-line mode may be similar to tap mode in thatnetwork traffic passes through the flow processing facility 102 fordetection and prevention of intrusions. Unlike tap mode, in-line modemay enforce network traffic to pass through it to reach other devices onthe network. In-line mode may facilitate the use of caching data streamsuntil there is enough for data reassembly which may allow detection andprevention of intrusions not otherwise easily detected. Examples ofin-line mode shown in FIG. 29 include interconnecting a firewall 514, awireless port 2918, a server 2904, and a VPN 2910.

As embodied in flow processing facility 102, intrusion detection andprevention functionality 2902 may be configured as an application oraction encompassed by unified threat management application 520. Assuch, intrusion detection and prevention 2902 may include one or more ofa firewall related application or action, an intrusion preventionapplication 518 or action, and a URL filter application 524 or action.

Intrusion detection and prevention 2902 may alternatively be embodied asa function or application within a network-connected computing devicesuch as a server 108. Such an application or software agent may monitorany and all activity of the server 108 on which it is installed,facilitating intrusion detection and prevention of such things asapplications, databases, file systems, operating systems, networkcommunication, and security policy. Intrusion detection and prevention2902 may identify and prevent intrusions by analyzing system calls,application logs, file-system modifications (binaries, password files,capability/acl databases) and other sever activities and states.

Any combination of independent and application based embodiments ofintrusion detection and prevention 2902 is possible within a network2900.

As can be seen in FIG. 29, virtual private networks 2910 and wirelessnetworks 2918 provide access to the internal network that may bypass thefirewall 514. An intrusion detection and prevention facility 2902 may beeffective at detecting and preventing intrusions through these networkinterfaces.

Intrusion detection and prevention 2902 may employ misuse type detectionalgorithms for detecting intrusion attempts at various levels of dataflow within a network. Such algorithms may be based on known intrusionsand stored in a database or table of intrusion signatures. Flowprocessing facility 102 may provide access to the various levels of dataflow 444 such as packets 402 and may also provide access to thesignatures. Application processor module 212 may process the data flows444 and the signatures to facilitate detection of intrusion attempts.

While signature based algorithms may detect known attacks as defined inthe signatures, processing logic such as the application processormodule 212 may be able to adapt or combine signatures to detect newattacks which share characteristics with old attacks, e.g., accessing‘cmd.exe’ via a HTTP GET request.

Intrusion detection may include the use of signatures that includeknowledge of semantics of session layer and application layer data flows444. The knowledge may be based on weighted network data flow contentthat is analyzed to develop the signatures.

Intrusion detection functions within intrusion detection and prevention2902 may include monitoring and analyzing both user and systemactivities, analyzing system configurations and vulnerabilities,assessing system and file integrity, recognizing patterns typical ofattacks, analyzing abnormal activity patterns, tracking user policyviolations, address matching, HTTP string and substring matching,generic pattern matching, analyzing TCP connections, packet anomalydetection, traffic anomaly detection, and TCP/UDP port matching, and thelike. Threats associated with user and system activities may include,without limitation worms, Trojans, spyware, keyloggers, and othermalware, rogue servers and applications that may have been unknowinglyadded to the network.

Intrusion detection and prevention 2902 may employ an anomaly basedtechnique for identifying intrusions of traffic or application contentpresumed to be different from ‘normal’ activity on the network.Anomaly-based intrusion detection and prevention 2902 configurations mayachieve this with self-learning such as a self organizing map or aKohonen map.

In anomaly detection, a system administrator may define a baseline, ornormal, state of the network's traffic load, breakdown, protocol, andtypical packet size. Intrusion detection and prevention 2902 may monitornetwork segment activity to compare it to the normal baseline and lookfor anomalies. Anomaly detection within intrusion detection andprevention 2902 may use network protocol analysis to detect anomaliesthat may indicate an intrusion.

Protocol analysis may also include low level analysis of data flows suchas at the network or transport layer by looking at the behavior of wellknown protocols such as ARP, BGP, EGP, IGMP, IPv4, IPv6, IPX, MPLS,OSPF, RARP, RIP, XNS, IL, RTP, SPX, SCTP, TCP, IP, and ICMP. Examples ofnetwork or protocol level intrusions that may be detected by protocolanalysis include TCP SYN flooding, malformed IP packets (such as an outof range IP header) or malformed browser data blocks sent to a webserver.

To facilitate protocol analysis, the flow processing facility 102 mayinclude packet arrival time stamping, packet filtering, packettriggering, and the like. In an example and without limitation, anetwork configuration of the flow processing facility 102 for very highspeed networks like Gigabit Ethernet may include packet arrival timestamping to facilitate merging two or more data flows together fordetection and prevention. This may facilitate detecting intrusions thatdo not sufficiently impact one flow to trigger an intrusion.

Intrusion detection and prevention 2902 may additionally oralternatively encompass a rate based intrusion protection technique.This rate based technique applied in intrusion detection and prevention2902 may rely on learned thresholds for different parameters of networktraffic. This technique may prevent denial of service attacks anddistributed denial of service attacks. This technique may use a SOM orneural net algorithm in learning the traffic behavior over a period oftime and adjusting thresholds on different parameters of networktraffic.

In an embodiment, the machine learning logic 314 may supportcontinuously learning network traffic patterns of data flows 444 suchthat a prediction may be made as to how much traffic is expected thenext moment. In an example and without limitation, applying a rate basedintrusion detection and prevention technique may facilitate predictinghow many packets in all, how many IP packets, how many ARP packets, howmany new connections/second, how many packets/connection, how manypackets to a specific tcp/udp port, and so forth. Detection may activateintrusion prevention when a measured network traffic parameter isdifferent than that predicted.

Detection of rate-intrusion may be done using the adaptive thresholdswherein the thresholds may be adjusted based on time of day, day ofweek, and based on past stored traffic statistics. Prevention may bedone using one or more of several techniques such as those that areassociated with or comprise granular rate-limiting on the specificdimension of attack; source tracking; connection tracking; dark-addressfiltering; network scan filtering; port scan filtering; legitimate IPaddress validation; and any and all other techniques that facilitateavoiding false positives.

In addition to detection and prevention, intrusion detection andprevention 2902 may provide protection against protocol anomalies, knownattacks (e.g. probes, scans, backdoors), malicious code (e.g. worms,viruses, Trojans), peer-to-peer traffic and denial-of-service attacks.Intrusion detection and prevention 2902 may also enforce network flowpolicies and watch for suspicious connections such as IPv6 tunneling.Intrusion detection and prevention 2902 may also use a combination ofsignatures and behavioral heuristics to detect security threats forproviding zero-day attack protection.

Content filtering may be an employed technique in intrusion detectionand protection 2902 for both inbound and outbound network data flows444. Content filtering may include signature-based filters that can beupdated in real-time to facilitate guarding against newly determinedthreats. In addition, heuristic and artificial intelligence tools suchas self organizing maps and neural-networks may be leveraged to providecontent filtering against unknown (i.e. zero-day) threats. As an exampleand without limitation, a content filtering solution within intrusiondetection and prevention 2902 may allow customization of a securitypolicy 414 such as filtering out any outbound e-mail containingproprietary diagrams, confidentiality content, non-compliant content, orlegal liability items.

Health Insurance Portability and Accountability Act (HIPAA) has set outdetailed regulations on the confidentiality of patient records andkeeping them safe from unauthorized use or viewing. Given the electronicnature of most patient health information, such as insurance approvals,prescription requests, and medical histories, mishandling confidentialdata may violate HIPPA. Content filters and other prevention techniqueswithin intrusion detection and prevention 2902 may be configured tomanage the transfer of this information securely.

Intrusion detection and prevention 2902 may include sensors deployedinline with a network connection such that the data streams that arepassing through are analyzed for intrusions. Depending on the type andseverity of an intrusion detected, a prevention action may be taken suchas the data packets within the data stream may be dropped, or an alertmay be issued, or the intrusion may simply be logged for later analysis.Another such prevention action may be to route intruding data streams toa virtual network such that all the information related to the intrusionis tracked and captured to facilitate providing forensic reports.

Intrusion detection and prevention 2902 may include alerting through avariety of electronic means such as email, system logging, snmp,logfile, SMS-external, pager, application execution, process spawning,third-party application execution, SGMS, SMS via email, consoleupdating, instant messaging, and any other electronic signalingtechniques.

While smart mobile devices (i.e. phones, PDAs, etc.) enable abundant newapplications, they also increase the risk of an Internet-based securitybreach. As an example, a laptop that has been intruded can easilypropagate malicious code of the intrusion, impacting critical businessprocesses the next time it legitimately connects to the network.Providing intrusion detection and prevention 2902 inline withconnections to these smart mobile devices may eliminate additionalpotential sources of intrusion.

Referring again to FIG. 29, intrusion detection and prevention 2902 mayalso provide solutions for voice over IP (VoIP) 2922 as it relates to anetwork 2900. VoIP may be vulnerable to intrusion such as hackers,attacks, worms, and vulnerabilities native to the application. These andother vulnerabilities may open the VoIP solution up to eavesdropping,identity theft, fraud and denial of service. VoIP protocols may beprotected by intrusion detection and prevention 2902 through monitoringcontrol flow such as SIP, H.225 (for H.323), and MGCP since the controlflow is where the logic and policy enforcement take place. VoIP dataflow or “media” such as RTP and RTCP may be subject to intrusion from avariety of sources such as exploits that target Skype, SIP, H.323, aswell as vendor-specific VoIP phones. Integrating intrusion detection andprevention 2902 with VoIP may facilitate preventing most of thepotential threats.

In addition to detecting VoIP control flow attacks with statefulsignatures, intrusion detection and prevention 2902 may also provideprotocol anomaly detection. Examples of some detectable and preventableintrusions associated with VoIP include SIP related intrusions:non-standard method, wrong version, no colon after the command, methodoverflow, unknown header, chunk length overflow, wrong content length,max-forwards are too big, H:225 intrusions unknown command, and noprotocol ID.

Elements of the flow processing facility 102 may be depicted throughoutthe figures with respect to logical boundaries between the elements.According to software or hardware engineering practices, the modulesdepicted may be implemented as individual modules. However, the modulesmay also be implemented in an alternate fashion, with logical boundariesless clearly defined in the source code, object code, hardware logic, orhardware modules that implement the modules. All such implementationsare within the scope of the present invention.

It will be appreciated that the various steps identified and describedabove may be varied, and that the order of steps may be changed to suitparticular applications of the techniques disclosed herein. All suchvariations and modifications are intended to fall within the scope ofthis disclosure. As such, the depiction and/or description of an orderfor various steps should not be understood to require a particular orderof execution for those steps, unless required by a particularapplication, or explicitly stated or otherwise clear from the context.

It will be appreciated that the above processes, and steps thereof, maybe realized in hardware, software, or any combination of these suitablefor a particular application. The hardware may include a general purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device that may be configured to process electronicsignals. It will further be appreciated that the process may be realizedas computer executable code created using a structured programminglanguage such as C, an object oriented programming language such as C++,or any other high-level or low-level programming language (includingassembly languages, hardware description languages, and databaseprogramming languages and technologies) that may be stored, compiled orinterpreted to run on one of the above devices, as well as heterogeneouscombinations of processors, processor architectures, or combinations ofdifferent hardware and software. At the same time, processing may bedistributed across a camera system and/or a computer in a number ofways, or all of the functionality may be integrated into a dedicated,standalone image capture device or other hardware. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

It will also be appreciated that means for performing the stepsassociated with the processes described above may include any of thehardware and/or software described above. In another aspect, eachprocess, including individual process steps described above andcombinations thereof, may be embodied in computer executable code that,when executing on one or more computing devices, performs the stepsthereof.

While the invention has been disclosed in connection with certainpreferred embodiments, other embodiments will be recognized by those ofordinary skill in the art, and all such variations, modifications, andsubstitutions are intended to fall within the scope of this disclosure.Thus, the invention is to be understood in the broadest sense allowableby law.

1. A method in a flow processing facility for securing a computerresource, comprising: receiving a data flow; employing a set ofartificial neurons to make a determination, the determination indicatingwhich of a plurality of patterns is present in the data flow; accessinga configuration, the configuration associating zero or more actions witheach pattern of the plurality of patterns; executing the actions thatare associated with the patterns that the determination indicates, theactions modifying the data flow; and transmitting the data flow.